World Bank Partnerships cities_out.html

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Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Cover photos Julianne Baker Gallegos World Bank

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KNOWLEDGE PAPERS BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Partnership Report Written by Daniel Hoornweg and Mila Freire Edited by Daniel Hoornweg Mila Freire Julianne Baker Gallegos and Artessa Saldivar Sali July No

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Urban Development Series Produced by the World Bank Urban Development and Resilience Unit of the Sustainable Development Network the Urban Development Series discusses the challenge of urbanization and what it will mean for developing countries in the decades ahead

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The Series aims to explore and delve more substantively into the core issues framed by the World Bank Urban Strategy Systems of Cities Harnessing Urbanization for Growth and Poverty Alleviation

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Across the five domains of the Urban Strategy the Series provides focal point for publications that seek to foster better understanding of the core elements of the city system ii pro poor policies iii city economies iv urban land and housing markets sustainable urban environment and other urban issues germane to the urban development agenda for sustainable cities and communities

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Copyright World Bank All rights reserved Urban Development Resilience Unit World Bank Street NW Washington DC USA www

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worldbank

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org urban This publication is product of the staff of the World Bank Group

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It does not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent

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The World Bank does not guarantee the accuracy of the data included in this work

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This note is provided for information only

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The World Bank has no responsibility for the persistence or accuracy of URLs and citations for external or third party sources referred to in this publication and does not guarantee that any content is or will remain accurate or appropriate

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TABLE OF CONTENTS Abbreviations and Acronyms viii Foreword ix Acknowledgements Introduction xii xiii About This Report xiv About the Partnership PART I

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WHY URBAN SUSTAINABILITY MATTERS

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Sustainable Development in the Urban Century Key Messages Local Impacts Global Change Locking In Green Growth Defining Sustainable Cities The Urban Ecosystem How Can Cities Be Made More Sustainable

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Economics of Green Cities Key Messages Urban Density Efficiency and Productivity Incentives Business Opportunities and Challenges Recession Investing and Sustainable Finance The Need for Knowledge Co benefits of Reducing Greenhouse Gas Emissions PART II

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THE PATH TO SUSTAINABILITY

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Building Clean and Efficient Cities Key Messages Land Management and Policy Energy Buildings Photo Shutterstock IV URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Embodied Energy and Historic Buildings Transportation Water Solid Waste Management

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Building Adaptive and Resilient Cities Key Messages Climate Change Vulnerability in Urban Areas Adaptation Planning at the City Level Assessing Risks and Developing Resilience

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Measuring Urban Sustainability Key Messages Urban Metabolism Measuring Inputs and Outputs The Large Urban Areas Compendium Typology of the Largest Cities The Case for an Urban Resilience Index Tracking Progress with City Indicators PART III

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THE ROLE OF INSTITUTIONS AND PARTNERSHIPS

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Governance and Implementation Key Messages Local Government National Government Public Private Partnerships Multilateral Institutions Municipal Networks and Civil Society Participation in Urban Governance

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Learning and Innovation Key Messages Governments Communities and Informal Networks Private Sector The Development Community PART IV

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THE PATH FORWARD

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Next Steps Toward Sustainable Cities Next Steps for Cities and Their Partners Next Steps for the Sustainable Cities Partnership References Annexes BUILDING SUSTAINABILITY IN AN URBANIZING WORLD List of Tables

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Infrastructure Levels of Countries by Income

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The Melbourne Principles of Urban Sustainability

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Technology Based Initiatives in cities

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Examples of Policies to Improve Building Energy Efficiency

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Examples of Urban Metabolism Studies

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Categories of Urban Metabolism Parameters

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Examples of Private Participation in Public Services Key Challenges for Sustainable Urban Development

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Moving Forward in the Sustainable Cities Partnership List of Figures

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Shares of World Urban Population and Regional Totals

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Population Growth in the Largest Urban Areas

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Characteristics of Sustainable City

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Hierarchy Model for Developing Sustainable City

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Value of Urban Sustainability Initiatives for Different Stakeholders

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How Cities Are Reducing Emissions

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African Urbanization Trend

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Spatial Growth of Three African Cities

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Energy Consumption Sectors Across Sample of Cities

Label: 11, with a score of: 0.714594653046577
WordScorePoS
energy0.0492535935240667NN
consumption0.08955644059472NN
sector0NN
city0.607697021770652NN

Label: 7, with a score of: 0.473472792727518
WordScorePoS
energy0.494617163780726NN
consumption0NN
sector0NN
city0NN

Label: 12, with a score of: 0.462450622683184
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN
sector0.0169905820958638NN
city0NN

Label: 8, with a score of: 0.165619598861056
WordScorePoS
energy0NN
consumption0.147812295323721NN
sector0.0252035516550537NN
city0NN

Costs of Renewable Energy

Label: 7, with a score of: 0.93456124153335
WordScorePoS
cost0NN
renewable0.158624482267508JJ
energy0.494617163780726NN

Label: 12, with a score of: 0.26481498233942
WordScorePoS
cost0.0277064208763707NN
renewable0.0234335422829798JJ
energy0.133960988253756NN

Carbon Dioxide Emissions from Buildings

Label: 13, with a score of: 0.717098209907644
WordScorePoS
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
building0NN

Label: 17, with a score of: 0.366475591017413
WordScorePoS
carbon0NN
dioxide0NN
emission0NN
building0.149940347231585NN

Label: 11, with a score of: 0.365172420130493
WordScorePoS
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
building0.0747035830159326NN

Label: 7, with a score of: 0.305596224580771
WordScorePoS
carbon0.0625160381065121NN
dioxide0NN
emission0.0625160381065121NN
building0NN

Estimated Economic Mitigation Potential

Label: 8, with a score of: 0.453257553405173
WordScorePoS
estimate0NN
economic0.128463821595706JJ
mitigation0NN
potential0NN

Label: 10, with a score of: 0.364390618747647
WordScorePoS
estimate0NN
economic0.103276847978089JJ
mitigation0NN
potential0NN

Label: 9, with a score of: 0.351551668564586
WordScorePoS
estimate0.0221298700466866NN
economic0.0320338181740375JJ
mitigation0NN
potential0.0454743001434393NN

Label: 1, with a score of: 0.338680262025143
WordScorePoS
estimate0NN
economic0.0959899298575892JJ
mitigation0NN
potential0NN

Label: 11, with a score of: 0.332230733612492
WordScorePoS
estimate0NN
economic0.0389167984372011JJ
mitigation0.055245183797332NN
potential0NN

Energy Consumption in U

Label: 7, with a score of: 0.69481362444563
WordScorePoS
energy0.494617163780726NN
consumption0NN

Label: 12, with a score of: 0.654771243311216
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN

Label: 8, with a score of: 0.207639370753888
WordScorePoS
energy0NN
consumption0.147812295323721NN

Commercial Buildings of Different Ages

Label: 17, with a score of: 0.61215234222243
WordScorePoS
commercial0JJ
building0.149940347231585NN
age0.0153248097886055NN

Label: 1, with a score of: 0.418539111066123
WordScorePoS
commercial0JJ
building0NN
age0.112994637345193NN

Label: 16, with a score of: 0.406672254492792
WordScorePoS
commercial0JJ
building0.109790895760528NN
age0NN

Label: 3, with a score of: 0.330825590604909
WordScorePoS
commercial0JJ
building0NN
age0.0893142758861673NN

Waste Production Per Capita

Label: 12, with a score of: 0.840587569785927
WordScorePoS
waste0.138532104381853NN
production0.118934074671047NN
capita0.0332151194779746NN

Current Waste Production and Urbanization by Region

Label: 12, with a score of: 0.651641815623486
WordScorePoS
current0.0332151194779746JJ
waste0.138532104381853NN
production0.118934074671047NN
urbanization0NN
region0NN

Label: 1, with a score of: 0.413068288470084
WordScorePoS
current0JJ
waste0NN
production0NN
urbanization0NN
region0.184259548107002NN

Label: 11, with a score of: 0.411654615781331
WordScorePoS
current0JJ
waste0.0373517915079663NN
production0NN
urbanization0.146277152173395NN
region0NN

Projected Waste Production and Urbanization by Region in

Label: 12, with a score of: 0.687623221472783
WordScorePoS
project0.0664302389559493NN
waste0.138532104381853NN
production0.118934074671047NN
urbanization0NN
region0NN

Label: 1, with a score of: 0.391177972375706
WordScorePoS
project0NN
waste0NN
production0NN
urbanization0NN
region0.184259548107002NN

Label: 11, with a score of: 0.389839216457073
WordScorePoS
project0NN
waste0.0373517915079663NN
production0NN
urbanization0.146277152173395NN
region0NN

Physical Effects of Climate Change Identified by Cities

Label: 13, with a score of: 0.68783694054029
WordScorePoS
effect0NN
climate0.291881734282273NN
change0.402565546215053NN
city0NN

Label: 11, with a score of: 0.655890575906774
WordScorePoS
effect0NN
climate0.0229054731708393NN
change0.0315914058171786NN
city0.607697021770652NN

Megacities Threatened by Sea Level Rise and Storm Surges VI URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS

Label: 13, with a score of: 0.607570020262162
WordScorePoS
threaten0VB
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
urban0JJ
development0NN
knowledge0NN

Label: 11, with a score of: 0.588793898120915
WordScorePoS
threaten0VB
sea0NN
level0.00692515656684925NN
rise0.0268848280079943NN
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN

Essential Considerations for Resilience in Urban Systems

Label: 11, with a score of: 0.87856467136348
WordScorePoS
essential0JJ
resilience0.0373517915079663NN
urban0.27622591898666JJ
system0.0315914058171786NN

The Urban Metabolism of Amman Jordan

Label: 11, with a score of: 0.984707888374301
WordScorePoS
urban0.27622591898666JJ

Standard Urban Metabolism Classification System

Label: 11, with a score of: 0.926943512727578
WordScorePoS
standard0NN
urban0.27622591898666JJ
system0.0315914058171786NN

The World Largest Urban Areas

Label: 11, with a score of: 0.928469705038565
WordScorePoS
world0.00626002771693937NN
largest0JJS
urban0.27622591898666JJ

Typology of the Largest Urban Areas Based on Emissions and GDP

Label: 11, with a score of: 0.741370788133629
WordScorePoS
largest0JJS
urban0.27622591898666JJ
base0NN
emission0.0373517915079663NN
gdp0NN

Label: 13, with a score of: 0.463359947126702
WordScorePoS
largest0JJS
urban0JJ
base0NN
emission0.19598742448524NN
gdp0NN

Label: 14, with a score of: 0.341474338799401
WordScorePoS
largest0.0325700316331562JJS
urban0JJ
base0.058665107245718NN
emission0NN
gdp0.0531983120572613NN

Per Capita Carbon Dioxide Emissions from Four Major Chinese Cities

Label: 11, with a score of: 0.882576534037702
WordScorePoS
capita0.04477822029736NN
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
major0JJ
city0.607697021770652NN

Label: 13, with a score of: 0.391498040818888
WordScorePoS
capita0NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
major0.0291713033871158JJ
city0NN

Greenhouse Gas Intensity versus GDP Per Capita

Label: 7, with a score of: 0.823399860454492
WordScorePoS
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
intensity0.122412709674631NN
gdp0NN
capita0NN

Label: 13, with a score of: 0.405977604616043
WordScorePoS
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
intensity0NN
gdp0NN
capita0NN

Label: 12, with a score of: 0.301310038893746
WordScorePoS
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
intensity0NN
gdp0NN
capita0.0332151194779746NN

GDP and Greenhouse Gas Emissions by Country Income

Label: 13, with a score of: 0.673041055188298
WordScorePoS
gdp0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
country0NN
income0NN

Label: 10, with a score of: 0.339275484727129
WordScorePoS
gdp0NN
greenhouse0NN
gas0NN
emission0NN
country0NN
income0.166475342664294NN

Label: 7, with a score of: 0.524589158638614
WordScorePoS
gdp0NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN
country0NN
income0.0449973847138318NN

Carbon Dioxide Emissions versus Urbanization

Label: 13, with a score of: 0.737757733919066
WordScorePoS
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
urbanization0NN

Label: 11, with a score of: 0.55566889703214
WordScorePoS
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
urbanization0.146277152173395NN

Label: 7, with a score of: 0.3144004196719
WordScorePoS
carbon0.0625160381065121NN
dioxide0NN
emission0.0625160381065121NN
urbanization0NN

Impacts of Natural Hazards by Region

Label: 1, with a score of: 0.666629806567306
WordScorePoS
impact0NN
natural0.0376648791150643JJ
hazard0NN
region0.184259548107002NN

Label: 12, with a score of: 0.4132518669853
WordScorePoS
impact0.0866018859977851NN
natural0.0509717462875914JJ
hazard0NN
region0NN

Label: 3, with a score of: 0.374995349125716
WordScorePoS
impact0NN
natural0JJ
hazard0NN
region0.124837844401518NN

Label: 13, with a score of: 0.347696068116498
WordScorePoS
impact0.0437569550806737NN
natural0.0171695137813101JJ
hazard0.0548233071065585NN
region0NN

Social Media Impact on Collective Action

Label: 12, with a score of: 0.573151466491368
WordScorePoS
social0.0169905820958638JJ
media0.0542519672965169NNS
impact0.0866018859977851NN
action0.0121782716594324NN

Label: 1, with a score of: 0.471915540900827
WordScorePoS
social0.112994637345193JJ
media0NNS
impact0NN
action0.0269969049497485NN

Label: 10, with a score of: 0.39047818893182
WordScorePoS
social0.101310366625194JJ
media0NNS
impact0NN
action0.0145231653596925NN

Triangular Partnerships Leverage Members Comparative Advantages List of Boxes

Label: 17, with a score of: 1
WordScorePoS
triangular0.0978661089756501JJ
partnership0.3914644359026NN

Drivers of Urbanization

Label: 11, with a score of: 1
WordScorePoS
urbanization0.146277152173395NN

Key Concepts for Urban Sustainability

Label: 11, with a score of: 0.920442538099593
WordScorePoS
key0NN
urban0.27622591898666JJ
sustainability0NN

Finding the Energy for Growing Economies

Label: 7, with a score of: 0.910182201050713
WordScorePoS
energy0.494617163780726NN
grow0VB
economy0.0625160381065121NN

Label: 12, with a score of: 0.274365192101695
WordScorePoS
energy0.133960988253756NN
grow0.0339811641917276VB
economy0NN

Can Infrastructure Keep Up with Demand

Label: 9, with a score of: 0.914510970487751
WordScorePoS
infrastructure0.192202909044225NN
demand0.030745604615229NN

Engineer Definition of Sustainable System

Label: 2, with a score of: 0.675641374670032
WordScorePoS
sustainable0.00374515576585943JJ
system0.0756001353333673NN

Label: 1, with a score of: 0.486171910942569
WordScorePoS
sustainable0.00514687440545096JJ
system0.0519476926891779NN

Ecosystems and Ecosystem Services

Label: 15, with a score of: 0.894404466877539
WordScorePoS
ecosystem0.134075577362426NN
service0.0127200410376242NN

Economic Valuation of Ecosystem Services

Label: 8, with a score of: 0.46171153841494
WordScorePoS
economic0.128463821595706JJ
ecosystem0NN
service0.0197870712294258NN

Label: 15, with a score of: 0.457179241976002
WordScorePoS
economic0JJ
ecosystem0.134075577362426NN
service0.0127200410376242NN

Label: 1, with a score of: 0.437090861323413
WordScorePoS
economic0.0959899298575892JJ
ecosystem0NN
service0.0443555132287109NN

Label: 10, with a score of: 0.346415890644259
WordScorePoS
economic0.103276847978089JJ
ecosystem0NN
service0.00795378154685591NN

Payments for Ecosystem Services

Label: 15, with a score of: 0.82068144551861
WordScorePoS
ecosystem0.134075577362426NN
service0.0127200410376242NN

Case Study Urban Freshwater Resources in Los Angeles

Label: 11, with a score of: 0.858859479483445
WordScorePoS
study0NN
urban0.27622591898666JJ
freshwater0NN
resource0.0449504361447405NN

Label: 15, with a score of: 0.308064558915441
WordScorePoS
study0.0517339898971162NN
urban0JJ
freshwater0.0316735178673487NN
resource0.0317953333768269NN

The Push for Green Growth

Label: 8, with a score of: 0.822888549490072
WordScorePoS
green0JJ
growth0.208565600657229NN

Label: 10, with a score of: 0.441033823494459
WordScorePoS
green0JJ
growth0.11178243319132NN

Do Families Prefer the Suburbs

Label: 3, with a score of: 0.690581699607026
WordScorePoS
family0.0624189222007589NN

Label: 5, with a score of: 0.515135985988816
WordScorePoS
family0.0465610847355851NN

Label: 6, with a score of: 0.386093191910923
WordScorePoS
family0.0348974218717993NN

Case Study Benefits of Bus Rapid Transit in Bogotá

Label: 0, with a score of: 1

Clean Energy Investments Surging

Label: 7, with a score of: 0.947068597242165
WordScorePoS
clean0.125032076213024JJ
energy0.494617163780726NN
investment0.0383371018038178NN

Label: 12, with a score of: 0.192815925151363
WordScorePoS
clean0JJ
energy0.133960988253756NN
investment0NN

Case Study Low Carbon Urban Development in China

Label: 11, with a score of: 0.905382272253388
WordScorePoS
study0NN
carbon0.0373517915079663NN
urban0.27622591898666JJ
development0.0100241617629471NN

Case Study Systematizing Land Titles in Africa

Label: 15, with a score of: 0.905599472797051
WordScorePoS
study0.0517339898971162NN
land0.123873694868802NN
africa0IN

Connecting Transportation and Land Use Planning

Label: 11, with a score of: 0.610216697604944
WordScorePoS
transportation0.04477822029736NN
land0.0389167984372011NN
plan0.0583751976558017NN

Label: 15, with a score of: 0.591177133888862
WordScorePoS
transportation0NN
land0.123873694868802NN
plan0.0137637438743113NN

Market Based Incentives for Land Policy

Label: 15, with a score of: 0.500767288432697
WordScorePoS
market0NN
base0.0190167691930804NN
incentive0.0390772412228479NN
land0.123873694868802NN
policy0NN

Label: 17, with a score of: 0.412107041105467
WordScorePoS
market0.030649619577211NN
base0.017987180284335NN
incentive0.036961556179878NN
land0NN
policy0.0641521854453136NN

Label: 2, with a score of: 0.379555624478363
WordScorePoS
market0.0913569596328122NN
base0NN
incentive0NN
land0.0465651203624806NN
policy0NN

Label: 1, with a score of: 0.333685768693958
WordScorePoS
market0NN
base0.0442083771593746NN
incentive0NN
land0.0319966432858631NN
policy0.0450489732120807NN

Case Study Smart Homes in Stratford Ontario

Label: 1, with a score of: 0.707230649607044
WordScorePoS
study0NN
home0.0908430554521382NN

Label: 15, with a score of: 0.706982891063425
WordScorePoS
study0.0517339898971162NN
home0.0390772412228479NN

IBM Smart City Projects

Label: 11, with a score of: 0.987730317679545
WordScorePoS
city0.607697021770652NN
project0NN

The Commercial Building Retrofit Market Potential

Label: 17, with a score of: 0.65728061816621
WordScorePoS
commercial0JJ
building0.149940347231585NN
market0.030649619577211NN
potential0NN

Label: 16, with a score of: 0.399598211958844
WordScorePoS
commercial0JJ
building0.109790895760528NN
market0NN
potential0NN

Label: 2, with a score of: 0.33250550937205
WordScorePoS
commercial0JJ
building0NN
market0.0913569596328122NN
potential0NN

Label: 9, with a score of: 0.302755103187285
WordScorePoS
commercial0JJ
building0NN
market0.0377086395700813NN
potential0.0454743001434393NN

Case Study Reducing Operational Energy in the Empire State Building BUILDING SUSTAINABILITY IN AN URBANIZING WORLD

Label: 7, with a score of: 0.679741271930074
WordScorePoS
study0NN
reduce0.00838876601975825VB
energy0.494617163780726NN
building0NN
sustainability0NN
world0.00261936653839306NN

Label: 17, with a score of: 0.477741862873992
WordScorePoS
study0NN
reduce0.00335331148065841VB
energy0.0109843026967392NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

Label: 12, with a score of: 0.271413097825416
WordScorePoS
study0NN
reduce0.022306853314744VB
energy0.133960988253756NN
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN

Label: 16, with a score of: 0.32490486386205
WordScorePoS
study0NN
reduce0.0220985725470694VB
energy0NN
building0.109790895760528NN
sustainability0NN
world0NN

Label: 11, with a score of: 0.302438961473243
WordScorePoS
study0NN
reduce0.0200483235258943VB
energy0.0492535935240667NN
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN

Measuring Embodied Energy

Label: 7, with a score of: 0.914656853036033
WordScorePoS
measure0NN
energy0.494617163780726NN

Label: 12, with a score of: 0.247723582839711
WordScorePoS
measure0NN
energy0.133960988253756NN

Case Study Reusing Historic Mansions in Qufu and Zoucheng

Label: 6, with a score of: 0.84252896783219
WordScorePoS
study0NN
reuse0.103230094596636NN

The Urgent Need for Sustainable Urban Transport

Label: 11, with a score of: 0.940160710735944
WordScorePoS
urgent0JJ
sustainable0.00782503464617421JJ
urban0.27622591898666JJ
transport0.0747035830159326NN

Congestion Pricing

Label: 11, with a score of: 1
WordScorePoS
congestion0.0731385760866977NN

Glossary of Terms Related to Adaptation

Label: 17, with a score of: 0.700612484284406
WordScorePoS
term0.149793392961411NN
related0JJ
adaptation0NN

Label: 13, with a score of: 0.392775788034883
WordScorePoS
term0NN
related0.0168470026160727JJ
adaptation0.0671298309154199NN

Label: 7, with a score of: 0.350535536891399
WordScorePoS
term0.0749457204978913NN
related0JJ
adaptation0NN

Benefits of Integrating Mitigation and Adaptation

Label: 13, with a score of: 0.717994218687294
WordScorePoS
integrate0.0201523636533764VB
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN

Label: 11, with a score of: 0.649148282344783
WordScorePoS
integrate0.0537696560159885VB
mitigation0.055245183797332NN
adaptation0.04477822029736NN

Urbanization in Coastal Areas

Label: 14, with a score of: 0.87624123278694
WordScorePoS
urbanization0NN
coastal0.265991560286306JJ

Label: 11, with a score of: 0.481872703070036
WordScorePoS
urbanization0.146277152173395NN
coastal0JJ

Case Study Blackouts in India and the United States

Label: 13, with a score of: 0.793214704411042
WordScorePoS
study0NN
unite0.0828215106044499VB

Label: 15, with a score of: 0.495477095319245
WordScorePoS
study0.0517339898971162NN
unite0VB

Keeping Cities Safe in Extreme Weather

Label: 11, with a score of: 0.888555821700228
WordScorePoS
city0.607697021770652NN
safe0.0916218926833571JJ
extreme0JJ
weather0NN

Label: 1, with a score of: 0.374225667854101
WordScorePoS
city0NN
safe0JJ
extreme0.294526332970048JJ
weather0NN

Case Study Growing Risks and Multiple Stakeholders in Altos de Cazucá

Label: 17, with a score of: 0.560115866974766
WordScorePoS
study0NN
grow0VB
risk0NN
multiple0.048933054487825JJ
stakeholder0.0739231123597559NN

Label: 15, with a score of: 0.411606020635909
WordScorePoS
study0.0517339898971162NN
grow0.016202004209163VB
risk0.0223459295604043NN
multiple0JJ
stakeholder0NN

Label: 11, with a score of: 0.392486722623014
WordScorePoS
study0NN
grow0.0229054731708393VB
risk0.0631828116343571NN
multiple0JJ
stakeholder0NN

Incorporating Uncertainty in Adaptation Strategies

Label: 13, with a score of: 0.721413066406632
WordScorePoS
adaptation0.0671298309154199NN
strategy0.0201523636533764NN

Label: 11, with a score of: 0.370105190096799
WordScorePoS
adaptation0.04477822029736NN
strategy0NN

Label: 1, with a score of: 0.365395268587884
WordScorePoS
adaptation0NN
strategy0.0442083771593746NN

The Cost of Adapting Housing and Slums

Label: 11, with a score of: 0.96818975816913
WordScorePoS
cost0NN
housing0.219415728260093NN
slum0.146277152173395NN

Earth Observation

Label: 14, with a score of: 0.87200973331915
WordScorePoS
earth0.0977100948994686NN

Label: 11, with a score of: 0.399621389992319
WordScorePoS
earth0.04477822029736NN

Where Are the Borders of the Largest Cities

Label: 11, with a score of: 0.975520407963837
WordScorePoS
border0NN
largest0JJS
city0.607697021770652NN

Forecasting Climate Hazards

Label: 13, with a score of: 0.933169247237489
WordScorePoS
climate0.291881734282273NN
hazard0.0548233071065585NN

Label: 7, with a score of: 0.309557117671795
WordScorePoS
climate0.115011305411453NN
hazard0NN

Which Cities are Most at Risk from Climate Change

Label: 11, with a score of: 0.687499002159073
WordScorePoS
city0.607697021770652NN
risk0.0631828116343571NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.658184642871894
WordScorePoS
city0NN
risk0NN
climate0.291881734282273NN
change0.402565546215053NN

Urban Risk Assessment

Label: 11, with a score of: 0.954758858280638
WordScorePoS
urban0.27622591898666JJ
risk0.0631828116343571NN

Case Study Institutions and Adaptation in Louisiana and the Netherlands

Label: 16, with a score of: 0.877050948218713
WordScorePoS
study0NN
institution0.274477239401321NN
adaptation0NN

Label: 10, with a score of: 0.316734806870855
WordScorePoS
study0NN
institution0.0991236547760315NN
adaptation0NN

Governing the Twenty First Century City

Label: 11, with a score of: 0.938448805363476
WordScorePoS
century0NN
city0.607697021770652NN

Label: 13, with a score of: 0.338648143512816
WordScorePoS
century0.219293228426234NN
city0NN

Decentralization of Governance

Label: 16, with a score of: 1
WordScorePoS
governance0.107490825192354NN

Vision The New Agenda for Business

Label: 17, with a score of: 0.690716710720098
WordScorePoS
vision0.048933054487825NN
agenda0.0739231123597559NN
business0NN

Label: 9, with a score of: 0.511327348448742
WordScorePoS
vision0NN
agenda0NN
business0.0909486002868787NN

Label: 16, with a score of: 0.456480595483526
WordScorePoS
vision0NN
agenda0.0811931365363091NN
business0NN

Case Study The Chicago Infrastructure Trust

Label: 9, with a score of: 0.89225846586613
WordScorePoS
study0NN
infrastructure0.192202909044225NN

Label: 7, with a score of: 0.302376361060488
WordScorePoS
study0NN
infrastructure0.0651354046449052NN

Reviving the Urban Environmental Accords

Label: 11, with a score of: 0.948381182857787
WordScorePoS
urban0.27622591898666JJ
environmental0.0537696560159885JJ

Case Study Participatory City Planning in Chhattisgarh

Label: 11, with a score of: 0.985054069183287
WordScorePoS
study0NN
participatory0.055245183797332JJ
city0.607697021770652NN
plan0.0583751976558017NN

Case Study Filling the Information Power Gap in Slums of Pune

Label: 11, with a score of: 0.594066206279285
WordScorePoS
study0NN
information0NN
power0NN
gap0NN
slum0.146277152173395NN

Label: 9, with a score of: 0.703601106931243
WordScorePoS
study0NN
information0.0675706773907211NN
power0.0454743001434393NN
gap0.0602029956716497NN
slum0NN

Mobile Phones Sweep Asia

Label: 9, with a score of: 1
WordScorePoS
phone0.0602029956716497NN
asia0IN

Cities Learning from Cities

Label: 11, with a score of: 0.977888279168848
WordScorePoS
city0.607697021770652NN
learn0VB

Label: 4, with a score of: 0.196737868674198
WordScorePoS
city0NN
learn0.244520809605025VB

Businesses Benefit from Sharing Information

Label: 9, with a score of: 0.784210110364597
WordScorePoS
business0.0909486002868787NN
share0.0377086395700813NN
information0.0675706773907211NN

Label: 17, with a score of: 0.394018126588884
WordScorePoS
business0NN
share0.0766240489430275NN
information0.0219686053934784NN

Label: 12, with a score of: 0.309778906365991
WordScorePoS
business0.0409791940864438NN
share0NN
information0.0365348149782971NN

Knowledge City Creative City or Informational City

Label: 11, with a score of: 0.993777217457802
WordScorePoS
knowledge0NN
city0.607697021770652NN

The Urban Infrastructure Initiative

Label: 11, with a score of: 0.748416287081393
WordScorePoS
urban0.27622591898666JJ
infrastructure0.0194583992186006NN
initiative0NN

Label: 9, with a score of: 0.486491094374724
WordScorePoS
urban0JJ
infrastructure0.192202909044225NN
initiative0NN

Label: 7, with a score of: 0.398905974494365
WordScorePoS
urban0JJ
infrastructure0.0651354046449052NN
initiative0.0924643738905717NN

Channeling Specialized Expertise in Academia

Label: 17, with a score of: 1
WordScorePoS
expertise0.048933054487825NN

Organizing Information from Urban Conferences VII VIII URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Abbreviations and Acronyms Cities Climate Leadership Group CBD United Nations Convention on Biological Diversity CDM Clean Development Mechanism CNG Compressed natural gas CUD Connected Urban Development program EO Earth observation GCI Great Cities Institute GDP Gross domestic product GER Gross energy requirement GIS Geographic information systems IBSG Internet Business Solutions Group ICLEI International Council for Local Environmental Initiatives ICT Information and communications technology IFI international financial institution IPCC Intergovernmental Panel on Climate Change IRP integrated resource planning ISO International Organization for Standardization LCA Life cycle assessment MDB Multilateral development bank MSW Municipal solid waste OECD Organisation for Economic Co operation and Development PER Process energy requirement PES Payments for ecosystem services RFSC Reference Framework for Sustainable Cities SSD Smarter Sustainable Dubuque TDR Transferable Development Rights UCLG United Cities and Local Governments UEA Urban Environmental Accords UN United Nations UNEP United Nations Environmental Programme USC Urban Systems Collaborative USGBC U

Label: 11, with a score of: 0.735951587283787
WordScorePoS
organize0VB
information0NN
urban0.27622591898666JJ
conference0NN
development0.0100241617629471NN
knowledge0NN
city0.607697021770652NN
climate0.0229054731708393NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0.0229054731708393JJ
gas0NN
earth0.04477822029736NN
gdp0NN
gross0.0373517915079663JJ
domestic0.0315914058171786JJ
product0.0268848280079943NN
energy0.0492535935240667NN
geographic0JJ
system0.0315914058171786NN
internet0NN
business0NN
solution0NN
international0JJ
local0.0194583992186006JJ
environmental0.0537696560159885JJ
initiative0NN
communication0NN
technology0NN
institution0NN
intergovernmental0JJ
panel0NN
change0.0315914058171786NN
integrate0.0537696560159885VB
resource0.0449504361447405NN
plan0.0583751976558017NN
organization0NN
life0.0112376090361851NN
cycle0NN
multilateral0JJ
bank0NN
municipal0.0731385760866977JJ
waste0.0373517915079663NN
oecd0NN
economic0.0389167984372011JJ
process0NN
ecosystem0NN
service0.0269743071558856NN
framework0.0194583992186006NN
sustainable0.00782503464617421JJ
rights0NNS
government0NN
programme0NN

Label: 13, with a score of: 0.315138667217319
WordScorePoS
organize0VB
information0NN
urban0JJ
conference0NN
development0NN
knowledge0NN
city0NN
climate0.291881734282273NN
leadership0NN
unite0.0828215106044499VB
nation0.0671298309154199NN
convention0.0828215106044499NN
biological0JJ
diversity0NN
clean0JJ
mechanism0.0414107553022249NN
natural0.0171695137813101JJ
gas0.0671298309154199NN
earth0NN
gdp0NN
gross0JJ
domestic0JJ
product0NN
energy0.0123065238088614NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0.0671298309154199NN
international0.00726580379728728JJ
local0.0145856516935579JJ
environmental0JJ
initiative0NN
communication0NN
technology0NN
institution0NN
intergovernmental0.0828215106044499JJ
panel0.0548233071065585NN
change0.402565546215053NN
integrate0.0201523636533764VB
resource0NN
plan0.0291713033871158NN
organization0NN
life0.00842350130803634NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0NN
oecd0NN
economic0JJ
process0NN
ecosystem0NN
service0NN
framework0.0291713033871158NN
sustainable0JJ
rights0NNS
government0NN
programme0NN

Label: 7, with a score of: 0.288613708039875
WordScorePoS
organize0VB
information0NN
urban0JJ
conference0NN
development0NN
knowledge0NN
city0NN
climate0.115011305411453NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0.125032076213024JJ
mechanism0NN
natural0JJ
gas0.0749457204978913NN
earth0NN
gdp0NN
gross0JJ
domestic0JJ
product0NN
energy0.494617163780726NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0NN
international0.00540783824576293JJ
local0JJ
environmental0JJ
initiative0.0924643738905717NN
communication0NN
technology0.109914925284606NN
institution0NN
intergovernmental0JJ
panel0NN
change0.105749654845006NN
integrate0VB
resource0NN
plan0NN
organization0NN
life0.0188084899376888NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0.0625160381065121NN
oecd0NN
economic0JJ
process0NN
ecosystem0NN
service0.0451471467893167NN
framework0NN
sustainable0.0104774661535722JJ
rights0NNS
government0NN
programme0.0274787313211514NN

Label: 17, with a score of: 0.251170684173357
WordScorePoS
organize0VB
information0.0219686053934784NN
urban0JJ
conference0NN
development0.0469463607292177NN
knowledge0.0499801157438617NN
city0NN
climate0NN
leadership0.036961556179878NN
unite0.036961556179878VB
nation0.0299586785922821NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0.147846224719512NN
natural0JJ
gas0NN
earth0NN
gdp0NN
gross0.0249900578719309JJ
domestic0.0634082886861185JJ
product0.017987180284335NN
energy0.0109843026967392NN
geographic0.048933054487825JJ
system0.0211360962287062NN
internet0.0739231123597559NN
business0NN
solution0NN
international0.00648516099948979JJ
local0.0130185595639838JJ
environmental0JJ
initiative0.036961556179878NN
communication0.0499801157438617NN
technology0.109843026967392NN
institution0.0249900578719309NN
intergovernmental0JJ
panel0NN
change0NN
integrate0VB
resource0.0375923735942499NN
plan0.0130185595639838NN
organization0.0422721924574123NN
life0NN
cycle0NN
multilateral0.048933054487825JJ
bank0.036961556179878NN
municipal0JJ
waste0NN
oecd0NN
economic0JJ
process0NN
ecosystem0NN
service0NN
framework0.0130185595639838NN
sustainable0.0115176738164035JJ
rights0NNS
government0.048933054487825NN
programme0NN

Label: 12, with a score of: 0.221981473152035
WordScorePoS
organize0VB
information0.0365348149782971NN
urban0JJ
conference0NN
development0.022306853314744NN
knowledge0NN
city0NN
climate0NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0.0509717462875914JJ
gas0.0332151194779746NN
earth0NN
gdp0NN
gross0JJ
domestic0JJ
product0.0199423462679015NN
energy0.133960988253756NN
geographic0JJ
system0NN
internet0NN
business0.0409791940864438NN
solution0NN
international0.0023966944644375JJ
local0.0144336476662975JJ
environmental0.0598270388037045JJ
initiative0NN
communication0NN
technology0NN
institution0NN
intergovernmental0JJ
panel0NN
change0NN
integrate0.0199423462679015VB
resource0.0416785799329393NN
plan0.0144336476662975NN
organization0NN
life0.0250071479597636NN
cycle0.108503934593034NN
multilateral0JJ
bank0NN
municipal0JJ
waste0.138532104381853NN
oecd0.108503934593034NN
economic0.0433009429988926JJ
process0NN
ecosystem0.0234335422829798NN
service0.00666957305782837NN
framework0.028867295332595NN
sustainable0.0162522423871414JJ
rights0NNS
government0NN
programme0.0121782716594324NN

Label: 5, with a score of: 0.12864646094442
WordScorePoS
organize0VB
information0.02046578088155NN
urban0JJ
conference0.18234260265061NN
development0.0124956749436837NN
knowledge0NN
city0NN
climate0NN
leadership0.0688661930304451NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0.0285529457667019JJ
gas0NN
earth0NN
gdp0NN
gross0JJ
domestic0.0393804437159645JJ
product0NN
energy0NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0NN
international0.00402768349408688JJ
local0JJ
environmental0JJ
initiative0NN
communication0.0465610847355851NN
technology0.0409315617631NN
institution0NN
intergovernmental0JJ
panel0NN
change0NN
integrate0VB
resource0.0280166088658745NN
plan0NN
organization0NN
life0.0140083044329373NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0NN
oecd0NN
economic0.0727679293221751JJ
process0.0558185411034276NN
ecosystem0NN
service0.022416649027415NN
framework0NN
sustainable0.00390173629172995JJ
rights0.078760887431929NNS
government0NN
programme0.02046578088155NN

Label: 8, with a score of: 0.116447014180924
WordScorePoS
organize0VB
information0NN
urban0JJ
conference0NN
development0.0055149369084567NN
knowledge0NN
city0NN
climate0NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0JJ
gas0NN
earth0NN
gdp0NN
gross0.0410992516792148JJ
domestic0.0695218668857431JJ
product0.0591643007226199NN
energy0NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0NN
international0.00355521737836969JJ
local0.0214106369326177JJ
environmental0.0295821503613099JJ
initiative0NN
communication0NN
technology0NN
institution0.0410992516792148NN
intergovernmental0JJ
panel0NN
change0NN
integrate0.0295821503613099VB
resource0.0123650647908625NN
plan0NN
organization0.0347609334428716NN
life0.0123650647908625NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0NN
oecd0NN
economic0.128463821595706JJ
process0NN
ecosystem0NN
service0.0197870712294258NN
framework0.0428212738652355NN
sustainable0.00688808874408283JJ
rights0.0347609334428716NNS
government0NN
programme0.0180650490434051NN

Label: 16, with a score of: 0.112937430627232
WordScorePoS
organize0.107490825192354VB
information0.0241291244414115NN
urban0JJ
conference0NN
development0.0368309542451156NN
knowledge0NN
city0NN
climate0NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0JJ
gas0NN
earth0NN
gdp0NN
gross0JJ
domestic0JJ
product0NN
energy0NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0NN
international0.0189945307832458JJ
local0JJ
environmental0JJ
initiative0NN
communication0NN
technology0NN
institution0.274477239401321NN
intergovernmental0JJ
panel0NN
change0NN
integrate0VB
resource0NN
plan0NN
organization0NN
life0NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0NN
oecd0NN
economic0JJ
process0NN
ecosystem0NN
service0NN
framework0NN
sustainable0.00920028227269801JJ
rights0NNS
government0NN
programme0NN

Label: 15, with a score of: 0.0887014037995602
WordScorePoS
organize0VB
information0NN
urban0JJ
conference0NN
development0.0106357666046841NN
knowledge0NN
city0NN
climate0.032404008418326NN
leadership0NN
unite0VB
nation0NN
convention0NN
biological0JJ
diversity0NN
clean0JJ
mechanism0NN
natural0.016202004209163JJ
gas0NN
earth0.0316735178673487NN
gdp0NN
gross0JJ
domestic0JJ
product0.0190167691930804NN
energy0.0116130458375813NN
geographic0JJ
system0NN
internet0NN
business0NN
solution0NN
international0.00228545753063677JJ
local0.0275274877486226JJ
environmental0JJ
initiative0NN
communication0NN
technology0NN
institution0NN
intergovernmental0JJ
panel0NN
change0.0446918591208085NN
integrate0.0190167691930804VB
resource0.0317953333768269NN
plan0.0137637438743113NN
organization0NN
life0.00794883334420673NN
cycle0NN
multilateral0JJ
bank0NN
municipal0JJ
waste0NN
oecd0NN
economic0JJ
process0.0316735178673487NN
ecosystem0.134075577362426NN
service0.0127200410376242NN
framework0NN
sustainable0.00664197120025784JJ
rights0NNS
government0NN
programme0NN

Green Building Council WBCSD World Business Council for Sustainable Development WFEO World Federation of Engineering Organizations All dollar amounts are U

Label: 17, with a score of: 0.631312178286793
WordScorePoS
green0JJ
building0.149940347231585NN
world0.00418824502414673NN
business0NN
sustainable0.0115176738164035JJ
development0.0469463607292177NN
engineering0NN
organization0.0422721924574123NN
dollar0.048933054487825NN
amount0NN

Label: 16, with a score of: 0.431930422917136
WordScorePoS
green0JJ
building0.109790895760528NN
world0NN
business0NN
sustainable0.00920028227269801JJ
development0.0368309542451156NN
engineering0NN
organization0NN
dollar0NN
amount0.0548954478802642NN

dollars unless otherwise indicated

Label: 17, with a score of: 1
WordScorePoS
dollar0.048933054487825NN

FOREWORD About

Label: 0, with a score of: 1

billion people now live in urban areas and that number is expected to double in just years

Label: 11, with a score of: 0.866887329013774
WordScorePoS
people0.0161552501392041NNS
live0.0269743071558856VB
urban0.27622591898666JJ
expect0VB
double0RB

Label: 1, with a score of: 0.301625894917505
WordScorePoS
people0.0371911007748996NNS
live0.0739258553811849VB
urban0JJ
expect0VB
double0RB

With urbanization more people have access to basic services literacy good jobs and longer lives

Label: 4, with a score of: 0.569920340642652
WordScorePoS
urbanization0NN
people0.00720147981137839NNS
access0.00697628678506322NN
basic0.0433695758119029JJ
service0NN
literacy0.244520809605025NN
job0.0152653111258778NN
life0.0125233923809486NN

Label: 11, with a score of: 0.482012066407714
WordScorePoS
urbanization0.146277152173395NN
people0.0161552501392041NNS
access0.00626002771693937NN
basic0.0583751976558017JJ
service0.0269743071558856NN
literacy0NN
job0.0136980135278128NN
life0.0112376090361851NN

Label: 8, with a score of: 0.351298158454095
WordScorePoS
urbanization0NN
people0.0106656521351091NNS
access0.00344404437204141NN
basic0.0214106369326177JJ
service0.0197870712294258NN
literacy0NN
job0.135650868266471NN
life0.0123650647908625NN

Label: 1, with a score of: 0.331198220891148
WordScorePoS
urbanization0NN
people0.0371911007748996NNS
access0.00514687440545096NN
basic0.0639932865717261JJ
service0.0443555132287109NN
literacy0NN
job0.0225244866060403NN
life0.0184786920895884NN

Label: 9, with a score of: 0.318870572568977
WordScorePoS
urbanization0NN
people0.0186171553883251NNS
access0.0128821355880844NN
basic0.0480507272610562JJ
service0.0148023490369527NN
literacy0NN
job0.0902026255635679NN
life0NN

But urbanization also raises concerns about whether cities can finance enormous amounts of infrastructure for millions of new citizens adequately plan for land requirements provide basic services and do all of this in way that strengthens social capital preserves the integrity of the Earth ecosystems and prepares for the shocks of climate change

Label: 11, with a score of: 0.671802066217285
WordScorePoS
urbanization0.146277152173395NN
raise0NN
city0.607697021770652NN
amount0NN
infrastructure0.0194583992186006NN
million0CD
plan0.0583751976558017NN
land0.0389167984372011NN
provide0.0269743071558856VB
basic0.0583751976558017JJ
service0.0269743071558856NN
strengthen0.0179828714372571VB
social0.0458109463416786JJ
earth0.04477822029736NN
ecosystem0NN
shock0NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.48386276887675
WordScorePoS
urbanization0NN
raise0.0671298309154199NN
city0NN
amount0.0279982034978914NN
infrastructure0NN
million0CD
plan0.0291713033871158NN
land0NN
provide0VB
basic0JJ
service0NN
strengthen0.00673981184904291VB
social0JJ
earth0NN
ecosystem0NN
shock0NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 15, with a score of: 0.264640844225479
WordScorePoS
urbanization0NN
raise0NN
city0NN
amount0NN
infrastructure0NN
million0CD
plan0.0137637438743113NN
land0.123873694868802NN
provide0.0445201436316848VB
basic0.0137637438743113JJ
service0.0127200410376242NN
strengthen0VB
social0JJ
earth0.0316735178673487NN
ecosystem0.134075577362426NN
shock0NN
climate0.032404008418326NN
change0.0446918591208085NN

Label: 9, with a score of: 0.186496874366216
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The Climate Group Smart report estimated that globally ICT enabled solutions for smart grids Photo Curt Carnemark World Bank smart buildings and smart logistics and industrial processes can reduce greenhouse gas emissions by as much as

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This paper the first product of the Partnership for Sustainable Cities presents wide range of approaches for the different aspects of urban sustainability

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The world headlong rush to urbanize is now half complete

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Seemingly small things can have major impacts

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This report is one such small step for an influential and concerned group of partners

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Zoubida Allaoua Director Urban Disaster Risk Management Department URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Acknowledgements This report was prepared by team led by Daniel Hoornweg Mila Freire Julianne Baker Gallegos and Artessa Saldivar Sali under the overall direction of Abha Joshi Ghani and Sameh Wahba Managers of the Urban Development and Resilience Unit and Zoubida Allaoua Director of the Urban and Disaster Risk Management Department

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The main authors and contributors for each chapter are listed below

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This report was developed following wiki like approach in an effort to compile multiple issues and sectors relevant to sustainable urban development

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It is product of the contributions of over authors and is presented as input for further dialogue across sectors and to help frame the discussion on urban sustainability

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Introduction Daniel Hoornweg and Mila Freire World Bank

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Chapter Stéphane Hallegatte Daniel Hoornweg Mila Freire Julianne Baker Gallegos World Bank Mike Sanio World Federation of Engineering Organizations Soraya Smaoun and Sharon Gil United Nations Environment Programme UNEP

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Boxes and figures were contributed by Martyna Kurcz Jenn Alstom and Henry Jewell World Bank

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Chapter Dimitri Zenghelis London School of Economics and Mila Freire

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Chapter Daniel Hoornweg Mila Freire Pascaline Ndungu Guido Licciardi Sintana E

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Vergara Michael Levitsky Hari Dulal World Bank Kyra Appleby Carbon Disclosure Project CDP Soraya Smaoun Jacob Halcomb UNEP Maggie Comstock U

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Green Building Council USGBC and Hans Degraeuwe Degraeuwe Consulting NV

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Boxes and figures were contributed by Dan Mathieson Mayor of Stratford Michelle Cullen IBM Henry Jewell Katie McWilliams and Alex Stoicof World Bank

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Chapter Julianne Baker Gallegos Mila Freire World Bank and Kyra Appleby CDP

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Boxes and figures were contributed by Kyra Appleby CDP Anthony Bigio and Stéphane Hallegatte World Bank

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Chapter Artessa Saldivar Sali Daniel Hoornweg Mila Freire World Bank Chris Kennedy University of Toronto Patricia McCarney Global City Indicators Facility GCIF and Anthony Bigio World Bank

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Boxes and figures were contributed by Anna Burzykowska European Space Agency and Katie McWilliams World Bank

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Chapter Artessa Saldivar Sali Alexandra Le Courtois Dennis Linders Daniel Hoornweg World Bank and Tim Campbell Urban Age Institute

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Boxes and figures were contributed by Christian Kornevall World Business Council for Sustainable Development WBCSD Shin pei Tsay and David Livingston Carnegie Endowment for International Peace Professor Kwi Gon Kim Seoul National University and Brian English CHF International

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Chapter Daniel Hoornweg Mila Freire World Bank Chris Kennedy University of Toronto Jonathan Fink and Vivek Shandas Portland State University

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Chapter Daniel Hoornweg Mila Freire World Bank and Greg Clark

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Annex Contributed by Karen Stelzner Siemens

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Annex Contributed by Patricia McCarney GCIF

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Annex Contributed by Bill Bertera Institute for Sustainable Infrastructure ISI

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Annex Contributed by Mike Sanio Bill Bertera and Carol Bowers the World Federation of Engineering Organizations Committee on Technology WFEOComTech

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Annex Contributed by Anat Lewin World Bank

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Annex Contributed by Molly Webb The Climate Group

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Annex Contributed by Jen Hawes Hewitt and Nicola Walt Accenture

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Annex Contributed by Anna Burzykowska European Space Agency ESA

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The team is grateful for the detailed comments from peer reviewers R

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Mukami Kariuki Dean Cira Anna Wellenstein Valerie Santos Ranjan Bose Jeanette Lim World Bank Dimitri Zenghelis London School of Economics Kyra Appleby CDP Patricia McCarney GCIF Michelle Cullen IBM Soraya Smaoun UNEP Chris Kennedy University of Toronto Bruno Conquet Pablo Vaggione Stewart Chisolm and Geoff Cape Evergreen Brickworks Genie Birch PennIUR Matthew Lynch WBCSD and Maggie Comstock USGBC

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Comments on earlier drafts were received from Martyna KurczJenn Alstom Stéphane Hallegate Rob Lichtman Systems Jen Hawes Hewitt Accenture Kyra Appleby CDP Alexandra Le Courtois World Bank Robin Reid World Economic Forum Donna McIntire UNEP and Jonathan Fink University of Portland

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The report could not have been completed without the generous contributions of more than members in the Partnership for Sustainable Cities workshops and review processes

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The report drew largely from discussions and themes that emerged in each of these events

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Firms and their employees who contributed input to this document and participated in these workshops include Accenture Aecon Alstom Arup ASCE Association of American Geographers Clinton Climate Initiative CapGemini The Carbon Disclosure Project Cisco Cities Alliance Citiscope The Climate Group Deutsche Bank Future Cities Initiative GCIF GDF Suez GE Global Urban Development IBM ICLEI KPMG McKinsey Metropolis Microsoft Office of Science and Technology Policy PFD Media Philips PwC Siemens UNEP UN Habitat University of Pennsylvania University of Toronto USAID U

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Department of State The Value Web Veolia WBCSD and WEF WRI

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The team thanks colleagues who helped organize the partnership events and supported the development of the document Marcus Lee Dennis Linders Fernando Armendaris Laura De Brular and Adelaide Barra

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Special thanks are due to Anna Barnett for the editorial work and Renee Saunders for report layout and design

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Finally all authors and members of the Partnership for Sustainable Cities extend their deep appreciation to the millions of professionals practitioners and city residents who undertake the world most important job every day building and managing better cities

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We all benefit from your efforts

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XI INTRODUCTION Cities are hubs of global change and their global influence continues to grow

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Cities contribute significantly to global challenges like climate change and biodiversity loss

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At the same time cities experience impacts like climate change first and with greatest intensity

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Further cities are becoming leaders worldwide in efforts to address global environmental and social problems

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Some of the most important smaller scale agreements and partnerships emerging from Rio the United Nations Conference on Sustainable Development were initiated by or focused on cities

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Even as the conference reinforced the increasing difficulty of reaching consensus on global challenges it also saw smaller scale agreements and partnerships emerge

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Some of the most important microagreements focused on cities

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For example the host city of Rio de Janeiro unveiled its own low carbon growth strategy

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The impacts of city level agreements will not necessarily be smaller than those of national accords

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Many of the concrete steps toward sustainable development can and must be enacted by municipal governments for example efficient and adaptive building standards public transportation smart power grids or flood protections

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The Rio summit itself identified sustainable cities Photo Curt Carnemark World Bank as one of seven critical issues in coming decades

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And the other six adequate jobs energy food security and sustainable agriculture water oceans and disaster readiness and resilience demand solutions that will be conceived piloted and mainstreamed mainly in cities

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Developments in Rio showcased the pragmatism and enthusiasm associated with sustainable cities

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Reviewing progress since the first UN Conference on Environment and Development years before Rio found some remarkable improvements notably in recognition of the role played by local governments their willingness to cooperate and eagerness to share information and the emerging synergy between research business and the public sector

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The initiatives announced at Rio followed several regional partnerships on cities and climate change in places such as Germany Australia and Mexico Newman and Jennings

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Cities are increasingly recognized as priority for inclusive green growth particularly in rapidly growing cities where it is essential to avoid locking in inefficient urban forms

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Moving forward further solidifying relationships among partners and local governments is critical

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The issue of cities and climate change has been explored by academics policy makers and private sector entities Hoornweg Freire et al

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There is BUILDING SUSTAINABILITY IN AN URBANIZING WORLD now ample evidence to confirm the impacts of urban spatial forms operations and governance on greenhouse emissions and to demonstrate effective strategies for climate change mitigation and adaptation

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Substantial work toward quantifying how cities metabolize resources and obtain clear indicators that facilitate strategies to compare and monitor policy effectiveness is available

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Private sector partners want to harness the extraordinary opportunities for innovation and business development in cities while both public and private partners are closely engaged with city administrations

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Existing experiences toolkits and technologies that have been tested in cities around the world are ever more in demand

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Dialogue among cities and the kinds of partnerships that are developing from Rio have never been more relevant than they are today

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Among the urban partners emerging in recent years the foremost are cities themselves and their national representatives agencies and networks such as ICLEI Local Governments for Sustainability United Cities and Local Governments UCLG and Metropolis the Climate and Clean Air Coalition group of national government representatives multilateral development banks such as the InterAmerican Development Bank Asian Development Bank and the World Bank UN Habitat the United Nations Environmental Programme the World Federation of Engineering Organizations privatesector companies the academic community philanthropic organizations like the Rockefeller Foundation and the Clinton Climate Initiative and technical agencies like the Green Building Council and the Climate Development Program

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To support this movement the World Bank initiated the Partnership for Sustainable Cities group of leading urban actors with mission to collaborate on city development around the world and foster city led sustainable development

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This synthesis paper is product of the partnership early discussions

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About the Partnership The Partnership for Sustainable Cities aims to bring together actors in the private sector academia and international financial institutions IFIs and to help coordinate their efforts to build inclusive sustainable and resilient cities

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The Bank and other partners are well positioned to provide technical and financial backing for these efforts

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The idea of such partnership started as early as and was cemented during seminar in Washington DC in June

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Attended by representatives of private companies international organizations academic institutions and the World Bank the workshop invited participants to share their ongoing programs related to sustainable cities to consider establishing partnership for exchanging information and to discuss the need for common tools and case studies

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Three key questions were proposed What do we need to know

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How do we take into account the varied characteristics of cities in developing countries

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And what is the role of indicators in the context of city sustainability

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The June meeting and follow on discussions were rich in ideas and consensus as participants came to agree on an agenda for collaboration

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The participants saw clear benefits from partnership of local governments and institutions interested in sustainable cities and anticipated sharing information experiences and lessons learned

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Individual partners committed to pursue several specific initiatives including compendium of data on the world largest cities Chapter sustainability rating tool for infrastructure and other projects Chapter

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More generally the group agreed to learn more about existing solutions examine the role of the private sector explore opportunities to cooperate define common approaches and monitor progress toward the goals set at the meeting

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XIII XIV BUILDING SUSTAINABILITY IN AN URBANIZING WORLD About This Report The report is organized into eight chapters In this discussion paper members of the partnership have collaborated to identify and analyze the issues that guide their work together

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The report summarizes the sustainability issues faced by cities and points toward the road ahead

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It reviews successes in policy as well as investment and discusses what is needed to reach out to the rapidly growing cities of the developing world and make them effective users of existing knowledge

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Examples of programs established by the partners are described in both the text and the Annexes

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Chapter discusses urbanization and the growing global impact of cities reviews the widely accepted definitions of sustainability and sustainable cities and elaborates on the need for innovative approaches to the various aspects of sustainability

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Compiled from the contributions of over authors this document should not be considered comprehensive synthesis but rather work in progress

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It is an input for dialogue across sectors and for framing loose partnership platform

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The report and its writing process also exemplify the partnership efforts to coordinate multiple stakeholders and help them create more sustainable cities through series of constantly evolving actions

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By working together in the development of this paper the partners established common understanding of the key elements of strategy for urban sustainability

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This supports the partnership central mission fostering worldwide collaboration on city led sustainable development

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This report aims to be useful to the partners who contributed with knowledge and experiences to cities who may benefit from an honest discussion of what works and what needs improvement and to businesses and development practitioners entering the wide world of sustainable cities

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Chapter reviews the importance of urbanization for economic growth and the opportunity for low carbon investments to promote growth and job creation in developing countries

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Chapter discusses the ways in which policies dealing with land and urban form can promote greener growth as well as how cities can take advantage of the enormous demand for infrastructure to become cleaner and more efficient

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It summarizes issues related to energy efficiency buildings urban transport water and waste

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Chapter discusses climate change adaptation in cities

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Chapter debates how to measure improvements in urban sustainability

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It reviews the framework of urban metabolism for understanding the flow of materials and energy and explores the use of indicators to measure aspects of sustainability including risks and resilience as well as efficiency

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A new compendium of data from the world largest urban areas is introduced and basic typology of these cities is presented

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Chapter discusses the roles of different institutions in the governance and implementation of sustainable cities and Chapter considers how institutions can contribute to learning and innovation

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Chapter suggests next steps to move toward sustainable cities identifying possible paths forward with partners

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Why Urban Sustainability Matters Photo Tran Thi Hoa World Bank BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Sustainable Development in the Urban Century Key Messages ' Sustainable cities are critical to sustainable development given their position as engines of economic growth centers of population growth and resource consumption and crucibles of culture and innovation

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WordScorePoS
urban0JJ
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Label: 17, with a score of: 0.237747160133522
WordScorePoS
urban0JJ
sustainability0.036961556179878NN
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century0NN
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critical0.036961556179878JJ
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Label: 13, with a score of: 0.10304802161849
WordScorePoS
urban0JJ
sustainability0NN
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' Cities must adopt sustainable development policies as soon as possible because today infrastructure investments will be locked in for hundreds of years

Label: 11, with a score of: 0.795852175315902
WordScorePoS
city0.607697021770652NN
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Label: 9, with a score of: 0.330677978251598
WordScorePoS
city0NN
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hundred0CD

This is all the more urgent in developing countries that are rapidly urbanizing

Label: 15, with a score of: 0.815298576046958
WordScorePoS
urgent0.0633470357346975JJ
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' Sustainable cities should be defined broadly integrating environmental economic and social objectives and should be supported with comprehensive and customizable how to menu

Label: 11, with a score of: 0.839898977106022
WordScorePoS
sustainable0.00782503464617421JJ
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Label: 8, with a score of: 0.272782586475384
WordScorePoS
sustainable0.00688808874408283JJ
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' Making cities sustainable requires addressing knowledge gaps broadening participation across stakeholders and incentivizing behavioral change at the individual corporate and local government levels

Label: 11, with a score of: 0.686374359843263
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.51571512814402
WordScorePoS
city0NN
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As the environmentalist Lester Brown warned decades ago pond that will be covered by the exponential growth of water lilies in days is only half covered on the th day Brown

Label: 6, with a score of: 0.643844548656286
WordScorePoS
warn0VB
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Label: 1, with a score of: 0.597797153228165
WordScorePoS
warn0VB
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Label: 8, with a score of: 0.290921830717633
WordScorePoS
warn0VB
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Label: 12, with a score of: 0.195522093144144
WordScorePoS
warn0VB
decade0NN
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water0.186896403054502NN
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So we stand today with urbanization

Label: 11, with a score of: 0.948344324298754
WordScorePoS
stand0VB
urbanization0.146277152173395NN

Label: 17, with a score of: 0.317242876311415
WordScorePoS
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Almost all the growth that cities have experienced in the last years is about to double in the next to years see Box and Annex

Label: 11, with a score of: 0.919835933497173
WordScorePoS
growth0NN
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Label: 8, with a score of: 0.315693720889001
WordScorePoS
growth0.208565600657229NN
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Much of this growth will take place in low and middleincome cities where percent of the world urban population is expected to reside in

Label: 11, with a score of: 0.915822594690018
WordScorePoS
growth0NN
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Label: 8, with a score of: 0.254828831180601
WordScorePoS
growth0.208565600657229NN
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Africa Asia and Latin America will be home to majority of the world urban population while Europe North America and Oceania shares are projected to decline steadily until Figure

Label: 11, with a score of: 0.653223468266588
WordScorePoS
africa0IN
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Label: 10, with a score of: 0.479571851519325
WordScorePoS
africa0IN
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majority0.0792212041430275NN
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Since the first humans began living in groups that stayed in place while they tended crops and livestock ours has been history of urbanization

Label: 11, with a score of: 0.710618017248486
WordScorePoS
human0.0273960270556257JJ
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Label: 2, with a score of: 0.383931063634357
WordScorePoS
human0JJ
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Label: 13, with a score of: 0.338264858202342
WordScorePoS
human0.0205355488872215JJ
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Today big problems climate change financial shocks biodiversity loss soil degradation civil unrest potential pandemics wars and strife over resources are in part the by products of this urbanization

Label: 15, with a score of: 0.602582136000531
WordScorePoS
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Label: 13, with a score of: 0.592543989598254
WordScorePoS
climate0.291881734282273NN
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Label: 11, with a score of: 0.251003157926054
WordScorePoS
climate0.0229054731708393NN
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So too are many of humanity greatest accomplishments increased affluence better health and well being longer life expectancy culture and the arts technological and creative innovation and reducing the number of people living in extreme poverty from million in to million in

Label: 1, with a score of: 0.700984221572373
WordScorePoS
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Label: 3, with a score of: 0.328420590558329
WordScorePoS
humanity0NN
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Label: 9, with a score of: 0.302543945166083
WordScorePoS
humanity0NN
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Cities as permanent places of residence are as old as civilization itself

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

Damascus for example is believed to have been continuously inhabited since B

Label: 0, with a score of: 1

Contrast this to companies and even countries which come and go

Label: 12, with a score of: 1
WordScorePoS
company0.108503934593034NN
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The average life expectancy of Fortune company is mere to years

Label: 13, with a score of: 0.633900340938429
WordScorePoS
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Label: 10, with a score of: 0.533907503862255
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average0.146608885563018NN
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Label: 12, with a score of: 0.486209062636054
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Of today sovereign states only nine have existed freely and continuously since before

Label: 17, with a score of: 0.732093193330456
WordScorePoS
exist0.0599173571845643VB

Label: 11, with a score of: 0.547117426894691
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exist0.04477822029736VB

The size and economic might of city may ebb and flow but its connection to the land and integration with natural ecosystems is relatively permanent

Label: 11, with a score of: 0.832739866248049
WordScorePoS
size0NN
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Label: 15, with a score of: 0.322254469968523
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size0NN
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Label: 8, with a score of: 0.208920293120361
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size0.049270765107907NN
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Cities are the physical places PovcalNet http iresearch

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

worldbank

Label: 0, with a score of: 1

org PovcalNet povcalNet

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businessweek

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com chapter degeus

Label: 0, with a score of: 1

htm Drivers of Urbanization In simple terms urbanization is the result of movement of people from rural areas to urban areas Sattherthwaite et al

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Label: 17, with a score of: 0.288553234304314
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urbanization0NN
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Label: 2, with a score of: 0.248334690343235
WordScorePoS
urbanization0NN
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people0.0257736572183839NNS
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both within their own countries and trans nationally

Label: 0, with a score of: 1

The underlying cause is attraction to economic cultural social and educational opportunities along with the quality of life that city provides

Label: 11, with a score of: 0.810021268027461
WordScorePoS
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Label: 8, with a score of: 0.32676929043339
WordScorePoS
underlie0.0607878664258118VB
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Label: 10, with a score of: 0.302460935757471
WordScorePoS
underlie0.0488696285210061VB
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Rapidly urbanizing nations have history of economic expansion and shift in employment patterns from rural agricultural or pastoral activities to industrial service oriented or knowledge based activities

Label: 9, with a score of: 0.498606579675928
WordScorePoS
rapidly0RB
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Label: 2, with a score of: 0.490834267407318
WordScorePoS
rapidly0RB
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Label: 8, with a score of: 0.454922457999294
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rapidly0RB
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As result of such trends by percent of the BOX The sheer magnitude of population and investments in urban areas combined with the suite of services required to support them make cities intricate complex systems with equally complex problems

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WordScorePoS
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In order to address these problems it is important to understand the drivers of urbanization and how these affect the vulnerability of the city system to global change

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WordScorePoS
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urbanization0NN
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world gross domestic product GDP was generated by industry and services Sattherthwaite et al

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Label: 14, with a score of: 0.303155236750759
WordScorePoS
world0.0045533140757086NN
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Thus people evidently are moving toward the job opportunities offered in cities for higher quality of life which involves higher salary and less physically labor intensive jobs

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WordScorePoS
people0.0161552501392041NNS
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Label: 8, with a score of: 0.464543071750377
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people0.0106656521351091NNS
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Yet this is simplistic generalization as some of the world largest cities for example Buenos Aires Kolkata Mexico City Rio de Janeiro São Paulo and Seoul have had more people leaving than moving to the city during their most recent census periods

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WordScorePoS
world0.00626002771693937NN
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Counterexamples like these illustrate the importance of location and timespecific studies and data gathering to inform policy making at the national level

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It is also important to recognize that cities are dynamic systems growing prospering or declining according to macroeconomic policies international trade regimes shifting national and international migration patterns and impacts from disasters such as earthquakes droughts or wars Sattherthwaite et al

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Label: 10, with a score of: 0.287978121651491
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FIG

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Africa Shares of World Urban Population and Regional Totals Latin America Asia Europe Source Hoornweg and Bhada Tata in press

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD where we live or want to countries and companies are what we create largely to protect and serve our cities

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building0.0747035830159326NN
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city0.607697021770652NN

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building0.149940347231585NN
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The only path to sustainable development is through sustainable cities see Box

Label: 11, with a score of: 0.971911747367282
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sustainable0.00782503464617421JJ
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Yet most of the world media and political leadership focus on national and international geopolitical issues the economic crises in Europe climate change the Arab Spring the war on terror China ascendancy

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WordScorePoS
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We are very good at discussing global symptoms

Label: 13, with a score of: 0.560514348810473
WordScorePoS
global0.0375696197697663JJ

Arguably over the last several decades while the world attended to economic growth and geopolitical dynamics the exponential growth of cities Figure went largely unnoticed

Label: 11, with a score of: 0.699532960877689
WordScorePoS
decade0.055245183797332NN
world0.00626002771693937NN
attend0VB
economic0.0389167984372011JJ
growth0NN
city0.607697021770652NN
figure0NN

Label: 8, with a score of: 0.540680727311964
WordScorePoS
decade0NN
world0.00172202218602071NN
attend0VB
economic0.128463821595706JJ
growth0.208565600657229NN
city0NN
figure0NN

Label: 10, with a score of: 0.325613923009773
WordScorePoS
decade0NN
world0.00276879546803864NN
attend0VB
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BOX Only in the last to years have cities and urbanization entered the common political and policy discourse

Label: 11, with a score of: 0.93685523844236
WordScorePoS
city0.607697021770652NN
urbanization0.146277152173395NN
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This will be an urban century and the potential for poorly designed and rapidly growing cities is crucial challenge to sustainable development

Label: 11, with a score of: 0.937873982066616
WordScorePoS
urban0.27622591898666JJ
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poorly0RB
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city0.607697021770652NN
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challenge0.0947742174515357NN
sustainable0.00782503464617421JJ
development0.0100241617629471NN

Label: 13, with a score of: 0.239326085714685
WordScorePoS
urban0JJ
century0.219293228426234NN
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poorly0RB
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While some see the speed of urbanization as threat to the carrying capacity of our planet others emphasize the need to envision human settlements in more positive ways first to reduce per capita impacts but then to move to new and more exciting possibility where cities begin to be positive force for the ecological regeneration of their regions Newman and Jennings

Label: 11, with a score of: 0.918791682347891
WordScorePoS
urbanization0.146277152173395NN
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Label: 1, with a score of: 0.162695038251302
WordScorePoS
urbanization0NN
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region0.184259548107002NN

The discussion on urbanization and the potential threats and opportunities it presents is starting in earnest

Label: 11, with a score of: 0.750674109552002
WordScorePoS
urbanization0.146277152173395NN
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opportunity0.0164178645080222NN

Label: 10, with a score of: 0.432536119036556
WordScorePoS
urbanization0NN
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Key Concepts for Urban Sustainability 'Green growth refers to making growth processes more resource efficient cleaner and more resilient without necessarily slowing them Hallegate et al

Label: 8, with a score of: 0.603652507911903
WordScorePoS
key0NN
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slow0.0607878664258118JJ

Label: 11, with a score of: 0.487419236394463
WordScorePoS
key0NN
urban0.27622591898666JJ
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growth0NN
process0NN
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The focus is on what must happen over the next years before the world gets locked into patterns that would be prohibitively expensive and complex to modify

Label: 9, with a score of: 0.587681041301337
WordScorePoS
focus0.030745604615229NN
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Label: 12, with a score of: 0.538464864888685
WordScorePoS
focus0NN
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pattern0.0819583881728876NN
expensive0.0542519672965169JJ

Label: 7, with a score of: 0.477980392088581
WordScorePoS
focus0NN
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The short and the long term can be reconciled by offsetting short term costs and maximizing synergies and economic co benefits green growth shifts the production frontier by promoting innovation and harnessing potential synergies across sectors Hallegate et al

Label: 8, with a score of: 0.563565778405767
WordScorePoS
term0NN
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sector0.0252035516550537NN

Label: 17, with a score of: 0.437552045787928
WordScorePoS
term0.149793392961411NN
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Label: 10, with a score of: 0.39831594255653
WordScorePoS
term0.0396106020715138NN
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Label: 9, with a score of: 0.334595834142052
WordScorePoS
term0NN
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economic0.0320338181740375JJ
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Green policies that can be used to capture these co benefits include price based policies norms and regulation public production and direct investment information dissemination education and moral suasion industrial policies and innovation policies

Label: 9, with a score of: 0.507594127187236
WordScorePoS
green0JJ
policy0.011275328195446NN
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innovation0.122982418460916NN

Label: 4, with a score of: 0.205304896886008
WordScorePoS
green0JJ
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education0.244244978014045NN
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Label: 17, with a score of: 0.452051378038037
WordScorePoS
green0JJ
policy0.0641521854453136NN
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education0NN
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innovation0.0499801157438617NN

Label: 10, with a score of: 0.411501892004952
WordScorePoS
green0JJ
policy0.0848203862485078NN
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education0.0121171980355011NN
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'Green cities are seriously committed to becoming environmentally responsible

Label: 11, with a score of: 0.991805165446092
WordScorePoS
city0.607697021770652NN
environmentally0RB
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Many have under taken internal environmental audits to understand the impact of their policies and many have become certified under the European Union Econ Management and Audit Scheme

Label: 12, with a score of: 0.556662496131177
WordScorePoS
environmental0.0598270388037045JJ
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Label: 11, with a score of: 0.431622745563536
WordScorePoS
environmental0.0537696560159885JJ
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Cities such as Den Haag and London have calculated their ecological footprints and are using these measures as policy benchmarks Beatley

Label: 11, with a score of: 0.932381444960083
WordScorePoS
city0.607697021770652NN
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'Smart cities have adopted technical and information platforms to better manage the use of their resources improve management monitor developments develop new business models and help citizens to make informed decisions about the use of resources

Label: 11, with a score of: 0.759317801058427
WordScorePoS
city0.607697021770652NN
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'Resilient cities have the ability to respond to natural disasters and system shocks and can provide reliable services under wide set of unpredictable circumstances

Label: 11, with a score of: 0.846409154035083
WordScorePoS
city0.607697021770652NN
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service0.0269743071558856NN
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set0NN
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Label: 7, with a score of: 0.188860007072442
WordScorePoS
city0NN
ability0NN
natural0JJ
disaster0NN
system0NN
shock0NN
provide0VB
reliable0.149891440995783JJ
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Label: 1, with a score of: 0.325116162128527
WordScorePoS
city0NN
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natural0.0376648791150643JJ
disaster0.0519476926891779NN
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These are cities that have built in systems such as diverse transport and land use that can adapt to change Newman

Label: 11, with a score of: 0.816500013257818
WordScorePoS
city0.607697021770652NN
build0VB
system0.0315914058171786NN
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land0.0389167984372011NN
change0.0315914058171786NN

Label: 13, with a score of: 0.418986212618396
WordScorePoS
city0NN
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WHY URBAN SUSTAINABILITY MATTERS Tokyo Tokyo Total Population of Largest Urban Areas Tokyo

Label: 11, with a score of: 0.9000557114393
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
total0NN
population0.0194583992186006NN
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million projected ai Mumb Population millions Delhi FIG

Label: 10, with a score of: 0.702550994729298
WordScorePoS
project0NN
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Label: 12, with a score of: 0.421029122045844
WordScorePoS
project0.0664302389559493NN
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Label: 6, with a score of: 0.407135566352893
WordScorePoS
project0.041835863322702NN
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Population Growth in the Largest Urban Areas Dhaka aulo São exico City York tta New Calcu hai ork City bai lo Shang Karach New Mexic Mum São Pau ork New City Mexic aulo São ork New Delhi asa Kinsh Lagos Cairo Manil Beijing Aire geles iro Dh ir Bueno Los An io de Jane A les Mumb Bueno Los Ange ta Tokyo ir ra Ka Jakar Istanbul Cair io de Ja Kobe tta eles zhou Kobe Osaka Calcu Los Ang Seoul enos Aires Guang saka Kobe Osaka Beijing Manila Bu ir re ul ris b Jane aris Mosco Laho Shenzhen ta Is Pa Rio Seoul Lagos Cairo Mosco Londo ta g ou Ja Chic Guangzh Delhi Shang Manila ndon arta Lo Jak hicag Beijing arachi K Istanb Dhaka Tehran Shang Paris Mosco enos Aires icago Ch Bu tta Calcu Beijing ka Kobe Angeles Osa Los hia BerlinPhiladelp de JaneirPetersburgico City mbai troit De Mu St

Label: 11, with a score of: 0.97537026194429
WordScorePoS
population0.0194583992186006NN
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largest0JJS
urban0.27622591898666JJ
city0.607697021770652NN
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paris0NN
st0IN

Label: 8, with a score of: 0.0899313270791913
WordScorePoS
population0.0428212738652355NN
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beij0NN
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Rio Mex Bosto CairoMancheste Tianjin ão Paulo mingham Bir hai Shang Calcutta Source developed by authors with data obtained from UN

Label: 0, with a score of: 1

Population Rank Photo Francis Dobbs World Bank BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Local Impacts Global Change As the world has become more urbanized the importance of urbanization and density for growth and prosperity has become widely accepted

Label: 13, with a score of: 0.535926045059199
WordScorePoS
population0NN
world0.00351929966767313NN
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Label: 11, with a score of: 0.506426892556765
WordScorePoS
population0.0194583992186006NN
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prosperity0.0731385760866977NN
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Label: 8, with a score of: 0.31587239020578
WordScorePoS
population0.0428212738652355NN
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global0.0220597476338268JJ
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growth0.208565600657229NN
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widely0RB

Label: 17, with a score of: 0.283806778617573
WordScorePoS
population0NN
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global0.0201198688839505JJ
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prosperity0NN
widely0RB

Label: 2, with a score of: 0.300000450948508
WordScorePoS
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Currently urbanized areas host more than half the of world

Label: 0, with a score of: 1

billion people and account for percent of the world GDP

Label: 14, with a score of: 0.516542137879529
WordScorePoS
people0.00705044110169491NNS
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Label: 12, with a score of: 0.486488367685574
WordScorePoS
people0.0119834723221875NNS
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They are seen as engines of growth contributing to poverty reduction improved living conditions cultural development and knowledge generation

Label: 8, with a score of: 0.532483905385134
WordScorePoS
growth0.208565600657229NN
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Label: 1, with a score of: 0.507197725929689
WordScorePoS
growth0.0519476926891779NN
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Label: 10, with a score of: 0.323758136888995
WordScorePoS
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Yet cities also affect the lion share of global and local environmental problems

Label: 11, with a score of: 0.941042094625142
WordScorePoS
city0.607697021770652NN
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Cities account for approximately percent of energy related carbon emissions worldwide and this is expected to increase to percent by with most of the increase coming from rapidly urbanizing countries such as China and India

Label: 11, with a score of: 0.679451721066295
WordScorePoS
city0.607697021770652NN
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Label: 7, with a score of: 0.576301821361863
WordScorePoS
city0NN
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Label: 13, with a score of: 0.303808941179598
WordScorePoS
city0NN
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Label: 12, with a score of: 0.272052791054532
WordScorePoS
city0NN
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By urban dwellers are expected to exceed percent of the global population

Label: 11, with a score of: 0.743846886022945
WordScorePoS
urban0.27622591898666JJ
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Label: 13, with a score of: 0.380848342794592
WordScorePoS
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Label: 10, with a score of: 0.366695074696359
WordScorePoS
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Hence BOX cities will continue to become more important as consumers of non renewable resources see Box and as contributors to greenhouse gas emissions

Label: 11, with a score of: 0.661084442349642
WordScorePoS
city0.607697021770652NN
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Label: 12, with a score of: 0.473221638652753
WordScorePoS
city0NN
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Label: 7, with a score of: 0.438588643330175
WordScorePoS
city0NN
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Label: 13, with a score of: 0.350405525744356
WordScorePoS
city0NN
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Consequently global agreements that seek to tackle threats such as climate change ozone depletion or hazardous waste must integrate cities as key players

Label: 13, with a score of: 0.629939685574281
WordScorePoS
global0.0375696197697663JJ
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Label: 11, with a score of: 0.615712824165173
WordScorePoS
global0.00501208088147356JJ
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Label: 12, with a score of: 0.146782725956438
WordScorePoS
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key0NN

Label: 7, with a score of: 0.301194986135348
WordScorePoS
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climate0.115011305411453NN
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Cities generally delegate and empower their national governments to negotiate and exert influence on their behalf but the resulting agreements by and large fall on cities to implement

Label: 11, with a score of: 0.971522250312132
WordScorePoS
city0.607697021770652NN
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agreement0NN
fall0NN
implement0.0273960270556257VB

In the field of green development number of multilateral institutions such as the World Bank the Organisation for Economic Co operation and Development OECD and the United Nations Environmental Programme UNEP as well as private sector actors such as McKinsey Siemens IBM and Cisco have begun focusing on the design and efficiency of cities

Label: 11, with a score of: 0.652001647082039
WordScorePoS
green0.04477822029736JJ
development0.0100241617629471NN
multilateral0JJ
institution0NN
world0.00626002771693937NN
bank0NN
economic0.0389167984372011JJ
oecd0NN
unite0VB
nation0NN
environmental0.0537696560159885JJ
programme0NN
private0JJ
sector0NN
actor0NN
focus0.0373517915079663NN
city0.607697021770652NN

Label: 16, with a score of: 0.280639427396439
WordScorePoS
green0JJ
development0.0368309542451156NN
multilateral0JJ
institution0.274477239401321NN
world0NN
bank0NN
economic0JJ
oecd0NN
unite0VB
nation0NN
environmental0JJ
programme0NN
private0JJ
sector0NN
actor0NN
focus0NN
city0NN

Label: 8, with a score of: 0.23882298962347
WordScorePoS
green0JJ
development0.0055149369084567NN
multilateral0JJ
institution0.0410992516792148NN
world0.00172202218602071NN
bank0NN
economic0.128463821595706JJ
oecd0NN
unite0VB
nation0NN
environmental0.0295821503613099JJ
programme0.0180650490434051NN
private0JJ
sector0.0252035516550537NN
actor0NN
focus0.0410992516792148NN
city0NN

Label: 17, with a score of: 0.331905934666772
WordScorePoS
green0JJ
development0.0469463607292177NN
multilateral0.048933054487825JJ
institution0.0249900578719309NN
world0.00418824502414673NN
bank0.036961556179878NN
economic0JJ
oecd0NN
unite0.036961556179878VB
nation0.0299586785922821NN
environmental0JJ
programme0NN
private0.0898760357768464JJ
sector0.0459744293658165NN
actor0NN
focus0NN
city0NN

Label: 12, with a score of: 0.304327788745084
WordScorePoS
green0.0332151194779746JJ
development0.022306853314744NN
multilateral0JJ
institution0NN
world0.00464349782489754NN
bank0NN
economic0.0433009429988926JJ
oecd0.108503934593034NN
unite0VB
nation0NN
environmental0.0598270388037045JJ
programme0.0121782716594324NN
private0JJ
sector0.0169905820958638NN
actor0.0542519672965169NN
focus0NN
city0NN

Economic development Finding the Energy for Growing Economies Most cities in the northern hemisphere recognize that energy costs will increase as the demand for energy rises in the rapidly growing economies of the global South

Label: 7, with a score of: 0.769113215394469
WordScorePoS
economic0JJ
development0NN
energy0.494617163780726NN
grow0VB
economy0.0625160381065121NN
city0NN
northern0JJ
recognize0VB
cost0NN
increase0.0162235147372888NN
demand0NN
rise0NN
rapidly0RB
global0.0251662980592747JJ
south0RB

Label: 11, with a score of: 0.412613393938333
WordScorePoS
economic0.0389167984372011JJ
development0.0100241617629471NN
energy0.0492535935240667NN
grow0.0229054731708393VB
economy0NN
city0.607697021770652NN
northern0JJ
recognize0VB
cost0NN
increase0.00323105002784081NN
demand0NN
rise0.0268848280079943NN
rapidly0RB
global0.00501208088147356JJ
south0RB

Label: 12, with a score of: 0.277325708845675
WordScorePoS
economic0.0433009429988926JJ
development0.022306853314744NN
energy0.133960988253756NN
grow0.0339811641917276VB
economy0NN
city0NN
northern0JJ
recognize0VB
cost0.0277064208763707NN
increase0.00958677785775002NN
demand0NN
rise0NN
rapidly0RB
global0.022306853314744JJ
south0RB

Label: 8, with a score of: 0.152653445154208
WordScorePoS
economic0.128463821595706JJ
development0.0055149369084567NN
energy0NN
grow0VB
economy0.0821985033584295NN
city0NN
northern0JJ
recognize0VB
cost0NN
increase0.00711043475673939NN
demand0NN
rise0NN
rapidly0RB
global0.0220597476338268JJ
south0RB

Developed country cities will likely adapt to this new scenario by reducing energy use and or innovating to make available new sustainable energy resources

Label: 7, with a score of: 0.748425444284879
WordScorePoS
develop0VB
country0NN
city0NN
scenario0NN
reduce0.00838876601975825VB
energy0.494617163780726NN
sustainable0.0104774661535722JJ
resource0NN

Label: 11, with a score of: 0.578359344935302
WordScorePoS
develop0VB
country0NN
city0.607697021770652NN
scenario0NN
reduce0.0200483235258943VB
energy0.0492535935240667NN
sustainable0.00782503464617421JJ
resource0.0449504361447405NN

Label: 12, with a score of: 0.258477728060433
WordScorePoS
develop0VB
country0NN
city0NN
scenario0NN
reduce0.022306853314744VB
energy0.133960988253756NN
sustainable0.0162522423871414JJ
resource0.0416785799329393NN

Regulatory frameworks or new policies are intended to provide the right incentives for structural change focusing on knowledge generation and service provision rather than industrial production

Label: 13, with a score of: 0.554515983573275
WordScorePoS
framework0.0291713033871158NN
policy0.0102677744436108NN
provide0VB
incentive0NN
change0.402565546215053NN
focus0.0279982034978914NN
knowledge0NN
generation0NN
service0NN
provision0NN
industrial0.0414107553022249JJ
production0NN

Label: 9, with a score of: 0.533842969508875
WordScorePoS
framework0NN
policy0.011275328195446NN
provide0.00740117451847634VB
incentive0NN
change0NN
focus0.030745604615229NN
knowledge0NN
generation0.0909486002868787NN
service0.0148023490369527NN
provision0NN
industrial0.318320101004075JJ
production0.0188543197850406NN

Label: 12, with a score of: 0.300085137759061
WordScorePoS
framework0.028867295332595NN
policy0.0203215381458133NN
provide0.0200087191734851VB
incentive0NN
change0NN
focus0NN
knowledge0NN
generation0.0819583881728876NN
service0.00666957305782837NN
provision0NN
industrial0JJ
production0.118934074671047NN

Mixed use neighbourhoods and interconnected systems grids to handle communications energy waste and transport will also be encouraged

Label: 7, with a score of: 0.776268649782776
WordScorePoS
system0NN
communication0NN
energy0.494617163780726NN
waste0.0625160381065121NN
transport0NN
encouraged0JJ

Label: 12, with a score of: 0.418276042838597
WordScorePoS
system0NN
communication0NN
energy0.133960988253756NN
waste0.138532104381853NN
transport0.0277064208763707NN
encouraged0JJ

Ultimately we would expect transition from systems that depend on the linear use of resources to highly interconnected systems that encourage the circular use of scarce resources

Label: 2, with a score of: 0.523716513465656
WordScorePoS
ultimately0RB
expect0.0357189919948307VB
system0.0756001353333673NN
depend0.0357189919948307VB
resource0.0358563662904843NN
highly0RB
encourage0VB
scarce0JJ

Label: 14, with a score of: 0.401269323481232
WordScorePoS
ultimately0RB
expect0VB
system0.0229784274580034NN
depend0.0651400632663124VB
resource0.0572167664438909NN
highly0RB
encourage0VB
scarce0JJ

This transition will require technical and social innovation

Label: 8, with a score of: 0.651511508478036
WordScorePoS
require0.104282800328615VB
technical0.0410992516792148JJ
social0.0504071033101074JJ
innovation0.0821985033584295NN

Label: 9, with a score of: 0.46542103298006
WordScorePoS
require0.0260040237236563VB
technical0.030745604615229JJ
social0.0188543197850406JJ
innovation0.122982418460916NN

Grids of communication energy water transport and monitoring sensors components of the stereo typical smart city will create intelligent self healing properties resulting in improved transport logistics with less congestion high efficiency resource flows and reduced costs and environmental impact

Label: 11, with a score of: 0.65864307716164
WordScorePoS
communication0NN
energy0.0492535935240667NN
water0.0458109463416786NN
transport0.0747035830159326NN
monitor0NN
city0.607697021770652NN
create0.0315914058171786VB
property0NN
result0NN
improve0.0273960270556257VB
congestion0.0731385760866977NN
resource0.0449504361447405NN
reduce0.0200483235258943VB
cost0NN
environmental0.0537696560159885JJ
impact0.0194583992186006NN

Label: 6, with a score of: 0.427929276044522
WordScorePoS
communication0NN
energy0.0153390539205676NN
water0.556409482163189NN
transport0NN
monitor0NN
city0NN
create0VB
property0NN
result0.0683326727248364NN
improve0.0511916925627245VB
congestion0NN
resource0.0104991907358033NN
reduce0.00936547854413134VB
cost0NN
environmental0JJ
impact0.0181797964452808NN

Label: 12, with a score of: 0.393734654814425
WordScorePoS
communication0NN
energy0.133960988253756NN
water0.186896403054502NN
transport0.0277064208763707NN
monitor0.0332151194779746NN
city0NN
create0.0234335422829798VB
property0NN
result0NN
improve0VB
congestion0NN
resource0.0416785799329393NN
reduce0.022306853314744VB
cost0.0277064208763707NN
environmental0.0598270388037045JJ
impact0.0866018859977851NN

Label: 7, with a score of: 0.308592514738393
WordScorePoS
communication0NN
energy0.494617163780726NN
water0NN
transport0NN
monitor0NN
city0NN
create0VB
property0NN
result0NN
improve0.0229264916384433VB
congestion0NN
resource0NN
reduce0.00838876601975825VB
cost0NN
environmental0JJ
impact0NN

Ecoinnovation will help cities of the developed world to be sustainable while creating the conditions for substantial improvement in the urban well being

Label: 11, with a score of: 0.944034243079542
WordScorePoS
city0.607697021770652NN
develop0VB
world0.00626002771693937NN
sustainable0.00782503464617421JJ
create0.0315914058171786VB
condition0NN
substantial0JJ
improvement0NN
urban0.27622591898666JJ

The cities of the global south face far more complicated challenge

Label: 11, with a score of: 0.981862002878776
WordScorePoS
city0.607697021770652NN
global0.00501208088147356JJ
south0RB
challenge0.0947742174515357NN

They too must find sustainable energy and more of it as rapidly growing young and increasingly affluent populations demand more energy to support industry and the consumer lifestyle

Label: 7, with a score of: 0.738625792940754
WordScorePoS
sustainable0.0104774661535722JJ
energy0.494617163780726NN
rapidly0RB
grow0VB
population0NN
demand0NN
support0.0188084899376888NN
industry0NN
consumer0NN
lifestyle0NN

Label: 12, with a score of: 0.572450155493077
WordScorePoS
sustainable0.0162522423871414JJ
energy0.133960988253756NN
rapidly0RB
grow0.0339811641917276VB
population0.0144336476662975NN
demand0NN
support0.00833571598658786NN
industry0NN
consumer0.325511803779102NN
lifestyle0.122937582259331NN

Label: 9, with a score of: 0.272947395396468
WordScorePoS
sustainable0.00901749491165909JJ
energy0.0540565419125769NN
rapidly0RB
grow0.0188543197850406VB
population0NN
demand0.030745604615229NN
support0.0277502419802315NN
industry0.181897200573757NN
consumer0NN
lifestyle0NN

However these cities are growing exponentially unlike the stable cities of the global north

Label: 11, with a score of: 0.988779650512142
WordScorePoS
city0.607697021770652NN
grow0.0229054731708393VB
stable0JJ
global0.00501208088147356JJ
north0RB

Informal settlers who are unable to find housing in the main city settle beyond organized boundaries often in marginal and under serviced areas without access to energy clean water transport education and health and sanitation services

Label: 11, with a score of: 0.608880055388777
WordScorePoS
housing0.219415728260093NN
city0.607697021770652NN
organize0VB
service0.0269743071558856NN
access0.00626002771693937NN
energy0.0492535935240667NN
clean0JJ
water0.0458109463416786NN
transport0.0747035830159326NN
education0NN
health0.0229054731708393NN
sanitation0NN

Label: 6, with a score of: 0.544298980824381
WordScorePoS
housing0NN
city0NN
organize0VB
service0.0084006124696649NN
access0.00731085509381114NN
energy0.0153390539205676NN
clean0.0348974218717993JJ
water0.556409482163189NN
transport0NN
education0NN
health0NN
sanitation0.334686906581616NN

Label: 7, with a score of: 0.409112767342332
WordScorePoS
housing0NN
city0NN
organize0VB
service0.0451471467893167NN
access0.0157161992303584NN
energy0.494617163780726NN
clean0.125032076213024JJ
water0NN
transport0NN
education0NN
health0NN
sanitation0NN

Label: 12, with a score of: 0.230229126440177
WordScorePoS
housing0NN
city0NN
organize0VB
service0.00666957305782837NN
access0.00232174891244877NN
energy0.133960988253756NN
clean0JJ
water0.186896403054502NN
transport0.0277064208763707NN
education0.0101607690729067NN
health0.0339811641917276NN
sanitation0NN

Label: 3, with a score of: 0.178612331327184
WordScorePoS
housing0NN
city0NN
organize0VB
service0.0100171139310145NN
access0.00610236075888593NN
energy0NN
clean0.020806307400253JJ
water0.0382775468083574NN
transport0NN
education0.0152605842304358NN
health0.191387734041787NN
sanitation0.0249430985430622NN

Label: 4, with a score of: 0.141633636553377
WordScorePoS
housing0NN
city0NN
organize0VB
service0NN
access0.00697628678506322NN
energy0NN
clean0JJ
water0NN
transport0NN
education0.244244978014045NN
health0NN
sanitation0NN

WHY URBAN SUSTAINABILITY MATTERS requires the capacity to welcome growing number of urban inhabitants without increasing disaster risks and environmental degradation

Label: 11, with a score of: 0.834666819684306
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
require0VB
capacity0.0112376090361851NN
grow0.0229054731708393VB
increase0.00323105002784081NN
disaster0.157957029085893NN
risk0.0631828116343571NN
environmental0.0537696560159885JJ
degradation0NN

Label: 15, with a score of: 0.273175523500282
WordScorePoS
urban0JJ
sustainability0NN
require0VB
capacity0.0158976666884135NN
grow0.016202004209163VB
increase0.0068563725919103NN
disaster0NN
risk0.0223459295604043NN
environmental0JJ
degradation0.221714625071441NN

In economic terms sustainable cities attempt to maximize and share the large economic benefits from increased population concentration Ciccone Ciccone and Hall World Bank while trying to avoid its negative externalities for example congestion loss of resources pollution and natural disaster risks

Label: 11, with a score of: 0.754425650501238
WordScorePoS
economic0.0389167984372011JJ
term0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN
share0NN
increase0.00323105002784081NN
population0.0194583992186006NN
concentration0NN
world0.00626002771693937NN
bank0NN
avoid0VB
congestion0.0731385760866977NN
loss0.0315914058171786NN
resource0.0449504361447405NN
pollution0.0315914058171786NN
natural0.0229054731708393JJ
disaster0.157957029085893NN
risk0.0631828116343571NN

Label: 8, with a score of: 0.232080702830619
WordScorePoS
economic0.128463821595706JJ
term0NN
sustainable0.00688808874408283JJ
city0NN
share0.0252035516550537NN
increase0.00711043475673939NN
population0.0428212738652355NN
concentration0NN
world0.00172202218602071NN
bank0NN
avoid0VB
congestion0NN
loss0NN
resource0.0123650647908625NN
pollution0NN
natural0JJ
disaster0NN
risk0NN

Label: 17, with a score of: 0.211019942873333
WordScorePoS
economic0JJ
term0.149793392961411NN
sustainable0.0115176738164035JJ
city0NN
share0.0766240489430275NN
increase0.00432344066632653NN
population0NN
concentration0NN
world0.00418824502414673NN
bank0.036961556179878NN
avoid0VB
congestion0NN
loss0NN
resource0.0375923735942499NN
pollution0NN
natural0JJ
disaster0NN
risk0NN

City design will be central to our ability to rise to society greatest challenges namely encouraging growth reducing poverty and increasing living standards while minimizing the consumption of scarce resources

Label: 11, with a score of: 0.61501552986743
WordScorePoS
city0.607697021770652NN
central0JJ
ability0NN
rise0.0268848280079943NN
society0NN
challenge0.0947742174515357NN
encourage0VB
growth0NN
reduce0.0200483235258943VB
poverty0.0164178645080222NN
increase0.00323105002784081NN
live0.0269743071558856VB
standard0NN
minimize0VB
consumption0.08955644059472NN
scarce0JJ
resource0.0449504361447405NN

Label: 8, with a score of: 0.454557311080346
WordScorePoS
city0NN
central0JJ
ability0.0607878664258118NN
rise0NN
society0.0591643007226199NN
challenge0.0347609334428716NN
encourage0.0695218668857431VB
growth0.208565600657229NN
reduce0.0055149369084567VB
poverty0.0722601961736204NN
increase0.00711043475673939NN
live0.00989353561471291VB
standard0NN
minimize0VB
consumption0.147812295323721NN
scarce0JJ
resource0.0123650647908625NN

Label: 12, with a score of: 0.405252380236395
WordScorePoS
city0NN
central0JJ
ability0.0409791940864438NN
rise0NN
society0NN
challenge0NN
encourage0.0468670845659596VB
growth0NN
reduce0.022306853314744VB
poverty0.0121782716594324NN
increase0.00958677785775002NN
live0VB
standard0.0409791940864438NN
minimize0.0664302389559493VB
consumption0.332151194779746NN
scarce0JJ
resource0.0416785799329393NN

Label: 1, with a score of: 0.321369699460427
WordScorePoS
city0NN
central0JJ
ability0NN
rise0NN
society0NN
challenge0NN
encourage0VB
growth0.0519476926891779NN
reduce0.0164833460638534VB
poverty0.269969049497485NN
increase0NN
live0.0739258553811849VB
standard0NN
minimize0VB
consumption0NN
scarce0JJ
resource0.0739147683583536NN

Fortunately cities can be efficient vehicles for sustainability as leaders are close to their citizens and are able to directly implement much needed policy changes on the ground

Label: 11, with a score of: 0.866519570381412
WordScorePoS
city0.607697021770652NN
vehicle0NN
sustainability0NN
implement0.0273960270556257VB
policy0.0136980135278128NN

Label: 12, with a score of: 0.398847757395323
WordScorePoS
city0NN
vehicle0.217007869186068NN
sustainability0.0409791940864438NN
implement0.0203215381458133VB
policy0.0203215381458133NN

Key segments of the green economy agenda such as buildings city form energy solid waste and urban transport are usually under the responsibility of the local or regional authority

Label: 11, with a score of: 0.772083069164785
WordScorePoS
key0NN
green0.04477822029736JJ
economy0NN
agenda0NN
building0.0747035830159326NN
city0.607697021770652NN
form0NN
energy0.0492535935240667NN
waste0.0373517915079663NN
urban0.27622591898666JJ
transport0.0747035830159326NN
responsibility0NN
local0.0194583992186006JJ
regional0.0315914058171786JJ

Label: 7, with a score of: 0.433216044207414
WordScorePoS
key0.0625160381065121NN
green0JJ
economy0.0625160381065121NN
agenda0NN
building0NN
city0NN
form0NN
energy0.494617163780726NN
waste0.0625160381065121NN
urban0JJ
transport0NN
responsibility0NN
local0JJ
regional0JJ

Label: 16, with a score of: 0.221657092592378
WordScorePoS
key0NN
green0JJ
economy0NN
agenda0.0811931365363091NN
building0.109790895760528NN
city0NN
form0.158049144643351NN
energy0NN
waste0NN
urban0JJ
transport0NN
responsibility0NN
local0JJ
regional0JJ

Label: 12, with a score of: 0.220904612075702
WordScorePoS
key0NN
green0.0332151194779746JJ
economy0NN
agenda0NN
building0NN
city0NN
form0NN
energy0.133960988253756NN
waste0.138532104381853NN
urban0JJ
transport0.0277064208763707NN
responsibility0NN
local0.0144336476662975JJ
regional0JJ

Label: 17, with a score of: 0.200125626207986
WordScorePoS
key0NN
green0JJ
economy0NN
agenda0.0739231123597559NN
building0.149940347231585NN
city0NN
form0NN
energy0.0109843026967392NN
waste0NN
urban0JJ
transport0.0249900578719309NN
responsibility0NN
local0.0130185595639838JJ
regional0.0422721924574123JJ

Innovation and efficiency may also come more naturally in cities

Label: 11, with a score of: 0.969796182768804
WordScorePoS
innovation0.0373517915079663NN
city0.607697021770652NN

Their high population density and compactness can allow for economies of scale and collaboration

Label: 8, with a score of: 0.527430082085771
WordScorePoS
population0.0428212738652355NN
density0NN
economy0.0821985033584295NN
scale0NN

Label: 11, with a score of: 0.390645635200532
WordScorePoS
population0.0194583992186006NN
density0.0731385760866977NN
economy0NN
scale0NN

Label: 10, with a score of: 0.363084662924919
WordScorePoS
population0.0860640399817412NN
density0NN
economy0NN
scale0NN

Label: 13, with a score of: 0.354354209319893
WordScorePoS
population0NN
density0NN
economy0.0839946104936743NN
scale0NN

They combine mix of specialization and diversity derived from concentration of people and economic activity that generate fertile environment for competition and innovation in ideas technologies and processes

Label: 8, with a score of: 0.430501954448924
WordScorePoS
mix0NN
diversity0NN
concentration0NN
people0.0106656521351091NNS
economic0.128463821595706JJ
activity0.0295821503613099NN
generate0VB
environment0.0591643007226199NN
innovation0.0821985033584295NN
idea0NN
technology0NN
process0NN

Label: 9, with a score of: 0.486718647752066
WordScorePoS
mix0NN
diversity0NN
concentration0NN
people0.0186171553883251NNS
economic0.0320338181740375JJ
activity0NN
generate0VB
environment0.0221298700466866NN
innovation0.122982418460916NN
idea0NN
technology0.0810848128688654NN
process0.0737171311497939NN

Label: 7, with a score of: 0.337575953642579
WordScorePoS
mix0.122412709674631NN
diversity0NN
concentration0NN
people0.0108156764915259NNS
economic0JJ
activity0NN
generate0VB
environment0NN
innovation0NN
idea0NN
technology0.109914925284606NN
process0NN

They produce and distribute the resources that provide better livelihoods for urban and rural residents alike

Label: 11, with a score of: 0.654486083449247
WordScorePoS
produce0VB
distribute0VB
resource0.0449504361447405NN
provide0.0269743071558856VB
livelihood0NN
urban0.27622591898666JJ
rural0.0373517915079663JJ

Label: 2, with a score of: 0.496536393800085
WordScorePoS
produce0.0357189919948307VB
distribute0VB
resource0.0358563662904843NN
provide0.0215170467912497VB
livelihood0.0504000902222448NN
urban0JJ
rural0.148975141598503JJ

Indeed there is already evidence that resource efficient innovations are being scaled up in cities both in developed and developing countries

Label: 11, with a score of: 0.912486043132113
WordScorePoS
evidence0NN
resource0.0449504361447405NN
innovation0.0373517915079663NN
scale0NN
city0.607697021770652NN
develop0VB
country0NN

Label: 10, with a score of: 0.171119193121298
WordScorePoS
evidence0.12939607756667NN
resource0NN
innovation0NN
scale0NN
city0NN
develop0VB
country0NN

This is because cities connect wide range of agents and assets including workers Take for example specialized restaurants

Label: 11, with a score of: 0.948481302131198
WordScorePoS
city0.607697021770652NN
wide0JJ
range0NN
asset0NN
include0VB
worker0NN

A large town can cater for specialized tastes and employ specialized chefs and specialized suppliers inviting competition and attracting innovation and immigration by discerning clientele

Label: 9, with a score of: 0.769474086058097
WordScorePoS
employ0.0454743001434393VB
attract0VB
innovation0.122982418460916NN

By contrast small town will not have met the threshold demand size to make specialist restaurant profitable and most eating establishments will cater to range of tastes by employing generalist chefs who use single supplier and appeal to the lowest common denominator

Label: 0, with a score of: 1

infrastructure consumers technologies resource flows suppliers cultures and histories

Label: 12, with a score of: 0.681164237871724
WordScorePoS
infrastructure0.028867295332595NN
consumer0.325511803779102NN
technology0NN
resource0.0416785799329393NN
culture0.0277064208763707NN
history0NN

Label: 9, with a score of: 0.46902385345957
WordScorePoS
infrastructure0.192202909044225NN
consumer0NN
technology0.0810848128688654NN
resource0.0185001613201543NN
culture0NN
history0NN

On the other hand their size and economic complexity mean that city specific problems such as congestion waste pollution education and crime require considered public intervention

Label: 11, with a score of: 0.787922644409156
WordScorePoS
size0NN
economic0.0389167984372011JJ
city0.607697021770652NN
congestion0.0731385760866977NN
waste0.0373517915079663NN
pollution0.0315914058171786NN
education0NN
crime0NN
require0VB
public0.0583751976558017NN
intervention0NN

Label: 8, with a score of: 0.276344915885793
WordScorePoS
size0.049270765107907NN
economic0.128463821595706JJ
city0NN
congestion0NN
waste0NN
pollution0NN
education0.0150723186962745NN
crime0NN
require0.104282800328615VB
public0NN
intervention0NN

Label: 12, with a score of: 0.270830879094249
WordScorePoS
size0NN
economic0.0433009429988926JJ
city0NN
congestion0NN
waste0.138532104381853NN
pollution0.0234335422829798NN
education0.0101607690729067NN
crime0NN
require0.0468670845659596VB
public0.028867295332595NN
intervention0NN

Label: 16, with a score of: 0.245297124366863
WordScorePoS
size0NN
economic0JJ
city0NN
congestion0NN
waste0NN
pollution0NN
education0.0201317944152378NN
crime0.214981650384708NN
require0VB
public0.0285977592242194NN
intervention0NN

Label: 4, with a score of: 0.227190160424139
WordScorePoS
size0NN
economic0JJ
city0NN
congestion0NN
waste0NN
pollution0NN
education0.244244978014045NN
crime0NN
require0VB
public0NN
intervention0NN

Indeed cities are constrained by many of the same forces as sovereign states the growing complexity of global systems is taxing current political structures at all levels

Label: 11, with a score of: 0.850650740961894
WordScorePoS
city0.607697021770652NN
force0NN
grow0.0229054731708393VB
global0.00501208088147356JJ
system0.0315914058171786NN
tax0NN
current0JJ
political0JJ
structure0NN
level0.00692515656684925NN

However cities are also able to act more independently and often are able to focus nascent leadership and the concerns of local residents

Label: 11, with a score of: 0.979417423943254
WordScorePoS
city0.607697021770652NN
focus0.0373517915079663NN
leadership0NN
local0.0194583992186006JJ

Efforts to reduce smoking and trans fats in food can be catalyzed by vanguard cities

Label: 11, with a score of: 0.836662098748341
WordScorePoS
reduce0.0200483235258943VB
food0NN
city0.607697021770652NN

Label: 2, with a score of: 0.459027033860076
WordScorePoS
reduce0.00799614078871077VB
food0.292342270824999NN
city0.0440683498519077NN

Label: 12, with a score of: 0.278827134044611
WordScorePoS
reduce0.022306853314744VB
food0.186896403054502NN
city0NN

Saving the bluefin tuna will probably require city to step forward and ban their sale

Label: 11, with a score of: 0.957674411691815
WordScorePoS
save0IN
require0VB
city0.607697021770652NN
ban0NN

New social norms such as gay marriage are often initiated by and in cities

Label: 11, with a score of: 0.953680573243498
WordScorePoS
social0.0458109463416786JJ
marriage0NN
city0.607697021770652NN

This potential for cities to lead wider change is often obvious only in retrospect

Label: 11, with a score of: 0.809229113047157
WordScorePoS
potential0NN
city0.607697021770652NN
lead0NN
change0.0315914058171786NN

Label: 13, with a score of: 0.509578690695437
WordScorePoS
potential0NN
city0NN
lead0NN
change0.402565546215053NN

In the Agenda agreement from the Rio Earth Summit the chapter on local government was the shortest but led to more action in the last years than any other chapter

Label: 14, with a score of: 0.496727984925966
WordScorePoS
agenda0NN
agreement0NN
earth0.0977100948994686NN
local0JJ
government0NN
shortest0.0531983120572613JJS
lead0NN
action0.0119417512090511NN

Label: 17, with a score of: 0.447951604215759
WordScorePoS
agenda0.0739231123597559NN
agreement0NN
earth0NN
local0.0130185595639838JJ
government0.048933054487825NN
shortest0JJS
lead0NN
action0.0109843026967392NN

Label: 16, with a score of: 0.389276420622923
WordScorePoS
agenda0.0811931365363091NN
agreement0.0464294830712827NN
earth0NN
local0JJ
government0NN
shortest0JJS
lead0NN
action0NN

Label: 15, with a score of: 0.368999846742335
WordScorePoS
agenda0NN
agreement0.0223459295604043NN
earth0.0316735178673487NN
local0.0275274877486226JJ
government0NN
shortest0JJS
lead0.016202004209163NN
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Locking In Green Growth The need for urban leadership is all the more urgent because choices made today will be multiplied over the next century or more

Label: 11, with a score of: 0.591229172554551
WordScorePoS
lock0NN
green0.04477822029736JJ
growth0NN
urban0.27622591898666JJ
leadership0NN
urgent0JJ
choice0NN
century0NN

Label: 13, with a score of: 0.527537335664316
WordScorePoS
lock0NN
green0.0335649154577099JJ
growth0NN
urban0JJ
leadership0NN
urgent0.0335649154577099JJ
choice0NN
century0.219293228426234NN

Label: 8, with a score of: 0.384138559007221
WordScorePoS
lock0NN
green0JJ
growth0.208565600657229NN
urban0JJ
leadership0NN
urgent0JJ
choice0NN
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Approximately

Label: 0, with a score of: 1

billion people will move into cities within just the next four decades United Nations and those people will need new infrastructure see Box and Table

Label: 11, with a score of: 0.836160894514674
WordScorePoS
people0.0161552501392041NNS
move0NN
city0.607697021770652NN
decade0.055245183797332NN
unite0VB
nation0NN
infrastructure0.0194583992186006NN

Label: 9, with a score of: 0.268425143531157
WordScorePoS
people0.0186171553883251NNS
move0NN
city0NN
decade0NN
unite0VB
nation0NN
infrastructure0.192202909044225NN

Label: 13, with a score of: 0.397557744153979
WordScorePoS
people0.0121096729954788NNS
move0.0414107553022249NN
city0NN
decade0.124232265906675NN
unite0.0828215106044499VB
nation0.0671298309154199NN
infrastructure0NN

The demand for housing and office space will continue to exceed supply leading to more informality and slum dwellings in the absence of vigorous policies to expand the supply of affordable solutions

Label: 11, with a score of: 0.707909161450389
WordScorePoS
demand0NN
housing0.219415728260093NN
space0.04477822029736NN
continue0.0537696560159885VB
exceed0VB
supply0NN
lead0NN
slum0.146277152173395NN
policy0.0136980135278128NN
expand0.0268848280079943VB
solution0NN

Label: 13, with a score of: 0.309452072222424
WordScorePoS
demand0NN
housing0NN
space0NN
continue0.0403047273067529VB
exceed0.0828215106044499VB
supply0NN
lead0NN
slum0NN
policy0.0102677744436108NN
expand0.0201523636533764VB
solution0.0671298309154199NN

China will double its housing stock between and and has already built some highway miles in just the last years

Label: 11, with a score of: 0.892896087340722
WordScorePoS
double0RB
housing0.219415728260093NN
stock0NN
build0VB

India is rapidly matching this growth

Label: 8, with a score of: 0.839894787333047
WordScorePoS
rapidly0RB
match0NN
growth0.208565600657229NN

Label: 10, with a score of: 0.450148455243547
WordScorePoS
rapidly0RB
match0NN
growth0.11178243319132NN

Energy consumption in developing countries will also increase sharply IEA and OECD

Label: 12, with a score of: 0.722197996459307
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN
develop0VB
country0NN
increase0.00958677785775002NN
oecd0.108503934593034NN

Label: 7, with a score of: 0.631506823035844
WordScorePoS
energy0.494617163780726NN
consumption0NN
develop0VB
country0NN
increase0.0162235147372888NN
oecd0NN

Label: 8, with a score of: 0.191517170036217
WordScorePoS
energy0NN
consumption0.147812295323721NN
develop0VB
country0NN
increase0.00711043475673939NN
oecd0NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD TABLE Low Income Countries Middle Income Countries High Income Countries Estimated municipal solid waste generation kg capita day

Label: 10, with a score of: 0.496295176053897
WordScorePoS
building0NN
sustainability0NN
world0.00276879546803864NN
income0.166475342664294NN
country0NN
middle0JJ
estimate0NN
municipal0JJ
waste0NN
generation0NN
capita0NN
day0NN

Label: 1, with a score of: 0.390297419911172
WordScorePoS
building0NN
sustainability0NN
world0.00257343720272548NN
income0.0442083771593746NN
country0NN
middle0JJ
estimate0NN
municipal0JJ
waste0NN
generation0NN
capita0NN
day0.25973846344589NN

Label: 12, with a score of: 0.315519945952044
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
income0NN
country0NN
middle0JJ
estimate0.0199423462679015NN
municipal0JJ
waste0.138532104381853NN
generation0.0819583881728876NN
capita0.0332151194779746NN
day0NN

Label: 17, with a score of: 0.2421728890785
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
income0.017987180284335NN
country0NN
middle0JJ
estimate0NN
municipal0JJ
waste0NN
generation0NN
capita0NN
day0NN

Label: 9, with a score of: 0.501840378509055
WordScorePoS
building0NN
sustainability0NN
world0.00257642711761688NN
income0.110649350233433NN
country0NN
middle0.0602029956716497JJ
estimate0.0221298700466866NN
municipal0JJ
waste0NN
generation0.0909486002868787NN
capita0NN
day0NN

Energy consumption kWh

Label: 7, with a score of: 0.69481362444563
WordScorePoS
energy0.494617163780726NN
consumption0NN

Label: 12, with a score of: 0.654771243311215
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN

Label: 8, with a score of: 0.207639370753888
WordScorePoS
energy0NN
consumption0.147812295323721NN

Percent of population in vulnerable housing Road density km per people

Label: 11, with a score of: 0.86148417324492
WordScorePoS
population0.0194583992186006NN
vulnerable0.0458109463416786JJ
housing0.219415728260093NN
road0.04477822029736NN
density0.0731385760866977NN
people0.0161552501392041NNS

Label: 1, with a score of: 0.308968206514062
WordScorePoS
population0NN
vulnerable0.112994637345193JJ
housing0NN
road0NN
density0NN
people0.0371911007748996NNS

Paved roads of total roads Tele density fixed lines mobile cellular subscriptions people Access to electricity of population Average Values Infrastructure Levels of Countries by Income Urban population in of total population Per capita GDP Estimated greenhouse gas emissions per capita tonnes year Finance indicators per capita Gross capital formation per capita Sources World Bank Data Indicators World Bank Hoornweg and Bhada Tata

Label: 11, with a score of: 0.470362706116098
WordScorePoS
road0.04477822029736NN
total0NN
density0.0731385760866977NN
line0.0373517915079663NN
people0.0161552501392041NNS
access0.00626002771693937NN
electricity0NN
population0.0194583992186006NN
average0NN
value0NN
infrastructure0.0194583992186006NN
level0.00692515656684925NN
country0NN
income0NN
urban0.27622591898666JJ
capita0.04477822029736NN
gdp0NN
estimate0NN
greenhouse0NN
gas0NN
emission0.0373517915079663NN
tonne0NN
finance0NN
gross0.0373517915079663JJ
source0NN
world0.00626002771693937NN
bank0NN
datum0NN

Label: 9, with a score of: 0.353462390395048
WordScorePoS
road0.0368585655748969NN
total0.0260040237236563NN
density0NN
line0.030745604615229NN
people0.0186171553883251NNS
access0.0128821355880844NN
electricity0.030745604615229NN
population0NN
average0NN
value0NN
infrastructure0.192202909044225NN
level0NN
country0NN
income0.110649350233433NN
urban0JJ
capita0NN
gdp0NN
estimate0.0221298700466866NN
greenhouse0NN
gas0NN
emission0NN
tonne0NN
finance0NN
gross0.030745604615229JJ
source0.0135141354781442NN
world0.00257642711761688NN
bank0NN
datum0.0454743001434393NN

Label: 10, with a score of: 0.350251819983642
WordScorePoS
road0NN
total0NN
density0NN
line0NN
people0.00571634317201755NNS
access0.00138439773401932NN
electricity0NN
population0.0860640399817412NN
average0.146608885563018NN
value0NN
infrastructure0NN
level0NN
country0NN
income0.166475342664294NN
urban0.0488696285210061JJ
capita0NN
gdp0NN
estimate0NN
greenhouse0NN
gas0NN
emission0NN
tonne0NN
finance0NN
gross0JJ
source0NN
world0.00276879546803864NN
bank0NN
datum0NN

Label: 13, with a score of: 0.314876138080634
WordScorePoS
road0NN
total0NN
density0NN
line0NN
people0.0121096729954788NNS
access0NN
electricity0NN
population0NN
average0.1656430212089NN
value0NN
infrastructure0NN
level0.0415277415054218NN
country0NN
income0NN
urban0JJ
capita0NN
gdp0NN
estimate0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
tonne0NN
finance0NN
gross0JJ
source0.0123065238088614NN
world0.00351929966767313NN
bank0NN
datum0NN

http data

Label: 17, with a score of: 0.851744941703599
WordScorePoS
datum0.0739231123597559NN

Label: 9, with a score of: 0.523956633971108
WordScorePoS
datum0.0454743001434393NN

worldbank

Label: 0, with a score of: 1

org indicator BOX Can Infrastructure Keep Up with Demand

Label: 9, with a score of: 0.914510970487751
WordScorePoS
infrastructure0.192202909044225NN
demand0.030745604615229NN

The rising demand for urban infrastructure housing water transport and energy is massive challenge for developing countries both from an environmental and financial point of view

Label: 11, with a score of: 0.623499466998822
WordScorePoS
rise0.0268848280079943NN
demand0NN
urban0.27622591898666JJ
infrastructure0.0194583992186006NN
housing0.219415728260093NN
water0.0458109463416786NN
transport0.0747035830159326NN
energy0.0492535935240667NN
challenge0.0947742174515357NN
develop0VB
country0NN
environmental0.0537696560159885JJ
view0NN

Label: 6, with a score of: 0.445754508525555
WordScorePoS
rise0.0251182378961834NN
demand0NN
urban0JJ
infrastructure0.0181797964452808NN
housing0NN
water0.556409482163189NN
transport0NN
energy0.0153390539205676NN
challenge0NN
develop0VB
country0NN
environmental0JJ
view0NN

Label: 7, with a score of: 0.444001213691472
WordScorePoS
rise0NN
demand0NN
urban0JJ
infrastructure0.0651354046449052NN
housing0NN
water0NN
transport0NN
energy0.494617163780726NN
challenge0.0528748274225028NN
develop0VB
country0NN
environmental0JJ
view0NN

Label: 12, with a score of: 0.316902490777775
WordScorePoS
rise0NN
demand0NN
urban0JJ
infrastructure0.028867295332595NN
housing0NN
water0.186896403054502NN
transport0.0277064208763707NN
energy0.133960988253756NN
challenge0NN
develop0VB
country0NN
environmental0.0598270388037045JJ
view0NN

Label: 9, with a score of: 0.282448561499341
WordScorePoS
rise0.0221298700466866NN
demand0.030745604615229NN
urban0JJ
infrastructure0.192202909044225NN
housing0NN
water0.0377086395700813NN
transport0.030745604615229NN
energy0.0540565419125769NN
challenge0NN
develop0VB
country0NN
environmental0.0221298700466866JJ
view0NN

It is estimated that yearly investments of

Label: 17, with a score of: 0.550871919437562
WordScorePoS
estimate0NN
investment0.0766240489430275NN

Label: 3, with a score of: 0.430661814089367
WordScorePoS
estimate0.059903311017139NN
investment0NN

Label: 9, with a score of: 0.430195938528142
WordScorePoS
estimate0.0221298700466866NN
investment0.0377086395700813NN

trillion would be needed for developing countries to satisfy basic needs and provide infrastructure for sustained growth EIB

Label: 9, with a score of: 0.752189133753281
WordScorePoS
develop0VB
country0NN
basic0.0480507272610562JJ
provide0.00740117451847634VB
infrastructure0.192202909044225NN
sustained0.120405991343299JJ
growth0.0260040237236563NN

Label: 8, with a score of: 0.438977588384202
WordScorePoS
develop0VB
country0NN
basic0.0214106369326177JJ
provide0VB
infrastructure0NN
sustained0JJ
growth0.208565600657229NN

Currently infrastructure investments public or private represent perhaps half that amount

Label: 9, with a score of: 0.631940494570146
WordScorePoS
infrastructure0.192202909044225NN
investment0.0377086395700813NN
public0.0160169090870187NN
private0.0368585655748969JJ
represent0VB
amount0NN

Label: 17, with a score of: 0.488445205729077
WordScorePoS
infrastructure0.0130185595639838NN
investment0.0766240489430275NN
public0.0390556786919513NN
private0.0898760357768464JJ
represent0VB
amount0NN

Label: 5, with a score of: 0.341555034403659
WordScorePoS
infrastructure0.024255976440725NN
investment0NN
public0.0727679293221751NN
private0.0558185411034276JJ
represent0VB
amount0NN

Financing for maintenance and efficient management has often proven elusive and attempts to attract private investors have had limited success except in few countries

Label: 17, with a score of: 0.691599203536894
WordScorePoS
financing0.036961556179878NN
management0NN
attract0.048933054487825VB
private0.0898760357768464JJ
limited0JJ
country0NN

Photo Julianne Baker Gallegos World Bank PART I

Label: 2, with a score of: 0.918499615257702
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

WHY URBAN SUSTAINABILITY MATTERS Photo Shutterstock Emerging economies that have to build the bulk of their infrastructure in the next two or three decades will be committing to either highor low resource intensity development paths

Label: 11, with a score of: 0.593785335394588
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
emerge0VB
economy0NN
build0VB
infrastructure0.0194583992186006NN
decade0.055245183797332NN
resource0.0449504361447405NN
intensity0NN
development0.0100241617629471NN

Label: 9, with a score of: 0.401490312499429
WordScorePoS
urban0JJ
sustainability0NN
emerge0VB
economy0NN
build0.030745604615229VB
infrastructure0.192202909044225NN
decade0NN
resource0.0185001613201543NN
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development0.0330049940546426NN

Label: 7, with a score of: 0.365811596018087
WordScorePoS
urban0JJ
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Label: 13, with a score of: 0.30460905831914
WordScorePoS
urban0JJ
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Urban infrastructure such as buildings transport systems or water systems generally has lifetime of at least years

Label: 6, with a score of: 0.633857145900991
WordScorePoS
urban0JJ
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water0.556409482163189NN

Label: 11, with a score of: 0.576240449444333
WordScorePoS
urban0.27622591898666JJ
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Label: 9, with a score of: 0.271079580693983
WordScorePoS
urban0JJ
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water0.0377086395700813NN

Label: 12, with a score of: 0.253205319799281
WordScorePoS
urban0JJ
infrastructure0.028867295332595NN
building0NN
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water0.186896403054502NN

Label: 17, with a score of: 0.239426595307545
WordScorePoS
urban0JJ
infrastructure0.0130185595639838NN
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In addition the location of infrastructure and building sites shapes the footprint of the city and its populations beyond the structure lifetime Gusdorf Hallegatte and Lahellec Gusdorf and Hallegatte

Label: 11, with a score of: 0.864245708207303
WordScorePoS
addition0NN
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Label: 17, with a score of: 0.312507225620679
WordScorePoS
addition0NN
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Label: 9, with a score of: 0.27444977130795
WordScorePoS
addition0.0368585655748969NN
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And urban policies are multiplied not only over time but socially

Label: 11, with a score of: 0.788839086570498
WordScorePoS
urban0.27622591898666JJ
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Label: 10, with a score of: 0.529140364664327
WordScorePoS
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Most policy decisions affect social networks in which individuals decisions on where to live where to work and how to commute have powerful effects on others entrenching attitudes toward for example bicycling or living near the city center

Label: 11, with a score of: 0.767187111678421
WordScorePoS
policy0.0136980135278128NN
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Label: 1, with a score of: 0.435948339550289
WordScorePoS
policy0.0450489732120807NN
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Label: 10, with a score of: 0.360144403780656
WordScorePoS
policy0.0848203862485078NN
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Thus fast expanding cities in the developing world present window of opportunity

Label: 11, with a score of: 0.969418895443156
WordScorePoS
expand0.0268848280079943VB
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The choices that are made in the next few decades will determine the structure that prevails in these cities for centuries

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WordScorePoS
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Label: 13, with a score of: 0.45551288644359
WordScorePoS
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High intensity development paths require less careful planning and are likely to be cheaper in the short run but extremely costly in the medium to long term

Label: 17, with a score of: 0.564020439668711
WordScorePoS
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Label: 7, with a score of: 0.482099972915433
WordScorePoS
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Label: 8, with a score of: 0.416700289081893
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The high inertia of city structures calls for long term planning based on long term outcomes often politically difficult but essential for sustainability

Label: 11, with a score of: 0.787925292118538
WordScorePoS
city0.607697021770652NN
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Label: 17, with a score of: 0.492680431747723
WordScorePoS
city0NN
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Although changing course later on is possibility the costs of such reversal would be enormous

Label: 13, with a score of: 0.931993423335085
WordScorePoS
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While more data and studies are required to understand the effectiveness of alternative approaches cities and their administrations cannot afford to wait for perfect information before making these sensitive decisions

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WordScorePoS
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Getting it right the first time can in fact accelerate urban economic growth in developing countries

Label: 8, with a score of: 0.527391764521857
WordScorePoS
time0NN
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Label: 11, with a score of: 0.493142921194725
WordScorePoS
time0NN
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Label: 10, with a score of: 0.508122062627299
WordScorePoS
time0.0607862199751165NN
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Label: 1, with a score of: 0.41969190325315
WordScorePoS
time0NN
accelerate0.120266261535276VB
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Unfortunately sustainable choices can be very difficult to fund given the scarcity of resources and unmet demands for basic services

Label: 6, with a score of: 0.698388124410461
WordScorePoS
sustainable0.00146217101876223JJ
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Label: 11, with a score of: 0.371532500143274
WordScorePoS
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Label: 1, with a score of: 0.360081775259031
WordScorePoS
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Defining Sustainable Cities How do we know if city is on track for sustainable development

Label: 11, with a score of: 0.989954498912405
WordScorePoS
sustainable0.00782503464617421JJ
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That term was first defined in the report Our Common Future World Commission on Environment and Development also known as the Brundtland Commission report

Label: 17, with a score of: 0.472039754882024
WordScorePoS
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Label: 12, with a score of: 0.508351789488641
WordScorePoS
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Label: 11, with a score of: 0.318954781659776
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The report widely used definition is Meeting the needs of the present without compromising the ability of future generations to meet their own needs

Label: 12, with a score of: 0.720521197942421
WordScorePoS
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Label: 2, with a score of: 0.443434360676158
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Label: 9, with a score of: 0.341975269068911
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BOX Engineer Definition of Sustainable System To an engineer sustainable system is one that is either in equilibrium or one that changes slowly at tolerable rate

Label: 1, with a score of: 0.590510476119614
WordScorePoS
sustainable0.00514687440545096JJ
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This concept of sustainability is best illustrated by natural ecosystems which consist of nearly closed loops that change slowly

Label: 13, with a score of: 0.825491926993378
WordScorePoS
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Label: 15, with a score of: 0.383445927303244
WordScorePoS
sustainability0NN
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For example in the food cycle of plants and animals plants grow in the presence of sunlight moisture and nutrients and are then consumed by insects and herbivores that in turn are eaten by successively larger animals

Label: 2, with a score of: 0.741991758280645
WordScorePoS
food0.292342270824999NN
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Label: 12, with a score of: 0.37408816426202
WordScorePoS
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Label: 15, with a score of: 0.513761748902886
WordScorePoS
food0.016202004209163NN
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The resulting natural waste products replenish nutrients which allow plants to grow and the cycle to begin again

Label: 12, with a score of: 0.742661949623689
WordScorePoS
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Label: 2, with a score of: 0.380417322505999
WordScorePoS
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Label: 15, with a score of: 0.355898429414988
WordScorePoS
result0NN
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If humans are to achieve sustainable development we will have to adopt patterns that reflect these natural processes

Label: 12, with a score of: 0.548168878352724
WordScorePoS
human0.0101607690729067JJ
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Label: 5, with a score of: 0.441234326816413
WordScorePoS
human0.0170753354211044JJ
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Label: 9, with a score of: 0.354471962492225
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The model of closed loop ecosystem was first proposed by the World Federation of Engineering Organizations in publication and other authors have since suggested modifications to this model

Label: 15, with a score of: 0.676515936396959
WordScorePoS
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Source Reprinted from WFEO ComTech

Label: 6, with a score of: 0.73415387760794
WordScorePoS
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Photo Michael Mertaugh World Bank Since there have been many efforts to explain and elaborate on what sustainable development means and at present at least two definitions are regularly used

Label: 1, with a score of: 0.613970220087747
WordScorePoS
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One emphasizes an engineeringoriented formulation that considers material flows and the impact that human consumption and production have on the local and global environment see Box

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WordScorePoS
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Label: 8, with a score of: 0.386206584817168
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A second definition suggests that sustainability must include wider set of characteristics including social and equity issues institutional capacity and participation and fiscal sustainability

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WordScorePoS
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In this second definition sustainability is often described as having three interdependent pillars economic environmental and social

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Label: 1, with a score of: 0.537360021575699
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Label: 10, with a score of: 0.484676180318457
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Label: 12, with a score of: 0.341903266398856
WordScorePoS
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For example the World Bank Urban and Resilience Management Unit currently defines sustainable cities as urban communities committed to improving the well being of their current and future residents while integrating economic environmental and social considerations

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The connections between the three pillars are especially evident in cities which function as integrated systems

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In some cities environmental degradation is already an obstacle to well being and poverty reduction

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WordScorePoS
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Label: 15, with a score of: 0.30496843017609
WordScorePoS
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Uncontrolled urban development may lead to reduction in soil permeability and drainage capacity that increases flood risks and the economic costs associated with them such as lack of economic competitiveness and poor well being

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Label: 10, with a score of: 0.370277794100407
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Label: 12, with a score of: 0.318444987168854
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It also disproportionately hurts the urban poor especially those living in informal settlements reducing their ability to accumulate capital and escape poverty Lall and Deichmann

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Label: 11, with a score of: 0.63740911465434
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The reverse is also true poverty may be cause of increased flood risks when lack of resources leads poor people to settle in marginal areas with limited access to basic services where drainage infrastructure cannot be extended and solid waste disposal is inadequate

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Label: 9, with a score of: 0.329988243300599
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Label: 12, with a score of: 0.24159387823921
WordScorePoS
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One useful set of markers for urban sustainability is the so called Melbourne Principles articulated at meeting which attempt to include the ecosystem dimension as well as the social and institutional characteristics that affect city performance Table

Label: 11, with a score of: 0.853318270655967
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set0NN
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Label: 1, with a score of: 0.270460687709741
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set0NN
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Label: 15, with a score of: 0.123055791984835
WordScorePoS
set0NN
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Label: 10, with a score of: 0.331776003097643
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In preparing this report members of the Partnership for Sustainable Cities worked to PART I

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Label: 17, with a score of: 0.54419254664092
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WHY URBAN SUSTAINABILITY MATTERS Principle Definition

Label: 11, with a score of: 0.852700049412013
WordScorePoS
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Label: 10, with a score of: 0.452576660519186
WordScorePoS
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Vision Provide long term vision for cities based on sustainability intergenerational social economic political equity and their individuality

Label: 11, with a score of: 0.746089027509886
WordScorePoS
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Label: 17, with a score of: 0.320073974289195
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Label: 8, with a score of: 0.185507097924649
WordScorePoS
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Label: 10, with a score of: 0.335417978059531
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Economy and society Achieve long term economic and social security move toward urban eco villages embedded into the bioregional economies encourage urban agriculture adopt true costing initiatives buy local

Label: 11, with a score of: 0.536398049983417
WordScorePoS
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Label: 8, with a score of: 0.401467385337806
WordScorePoS
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Label: 2, with a score of: 0.223545309183492
WordScorePoS
economy0NN
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Label: 17, with a score of: 0.218525422629539
WordScorePoS
economy0NN
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Label: 10, with a score of: 0.393402299130747
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economy0NN
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Biodiversity Recognize the value of biodiversity and natural ecosystems protect and restore them

Label: 15, with a score of: 0.886097903040003
WordScorePoS
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Label: 14, with a score of: 0.331147619768889
WordScorePoS
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Ecological footprints Enable communities to minimize their ecological footprints

Label: 12, with a score of: 0.546178956396912
WordScorePoS
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Modeling cities on ecosystems Build on the characteristics of ecosystems in the development and nurturing of healthy and sustainable cities

Label: 11, with a score of: 0.944585274130091
WordScorePoS
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Label: 15, with a score of: 0.218620236431007
WordScorePoS
city0NN
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Sense of place Recognize and build on the distinctive characteristics of cities including their human value and natural systems

Label: 11, with a score of: 0.913800541854577
WordScorePoS
sense0NN
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Empowerment Empower people and foster participation

Label: 5, with a score of: 0.90294612897088
WordScorePoS
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Partnerships Promote and enable cooperative networks towards common sustainable future

Label: 17, with a score of: 0.916314833329532
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Sustainable production and consumption Promote sustainable production and consumption through sound technologies and effective demand management

Label: 12, with a score of: 0.813383310718742
WordScorePoS
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Label: 8, with a score of: 0.368221435100114
WordScorePoS
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Governance and hope Enable continuous improvement based on accountability transparency and good governance

Label: 16, with a score of: 0.727592423721395
WordScorePoS
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Label: 17, with a score of: 0.395642182547331
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Label: 7, with a score of: 0.312940094126786
WordScorePoS
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develop their own shared sense of what defines sustainable city and what are its critical building blocks

Label: 11, with a score of: 0.896376441371794
WordScorePoS
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Label: 17, with a score of: 0.35719134839974
WordScorePoS
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Participants in Defining Sustainable Cities workshop in favored wide concept of sustainability going beyond environmental impacts alone

Label: 11, with a score of: 0.937069887253379
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However defining urban sustainability is complex task

Label: 11, with a score of: 0.966188480208834
WordScorePoS
urban0.27622591898666JJ
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Any given city sustainability is influenced by its historical and cultural context its goals livability or business development for example and its local geography and environmental conditions

Label: 11, with a score of: 0.886668608815916
WordScorePoS
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Moreover partners from different sectors preferred different definitions of sustainability at the city level

Label: 11, with a score of: 0.956901621320302
WordScorePoS
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TABLE The Melbourne Principles of Urban Sustainability Sources UNEP Newman and Jennings

Label: 11, with a score of: 0.819739241204932
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Label: 10, with a score of: 0.435082475410765
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program for sustainable cities

Label: 11, with a score of: 0.995778113359572
WordScorePoS
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Participants argued that there is hierarchy among the actions to be taken as cities need to focus first on basic service provision before tackling FIG

Label: 11, with a score of: 0.932512707493206
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other levels of design and governance

Label: 16, with a score of: 0.947858355204983
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Figure Characteristics of Sustainable City expresses this idea in draft model

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Rather than one size fits all definition one can use word cloud to represent the partnership views Figure

Label: 17, with a score of: 0.957281086618137
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The figure lists key characteristics for urban sustainability as defined by the workshop participants

Label: 11, with a score of: 0.882563484736078
WordScorePoS
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In addition Chapter reviews in detail various ways that city sustainability can be measured

Label: 11, with a score of: 0.938303649399557
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Also discussed at the workshop was the need for comprehensive and customizable how to Source World Bank NOTE Size of text corresponds to frequency of each word in definitions of sustainable cities suggested by participants at the World Bank workshop Defining Sustainable Cities Washington DC January

Label: 11, with a score of: 0.95442892994662
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD The hierarchy framework draws from the three pillars of sustainable development economic competitiveness environmental sustainability and social equity

Label: 17, with a score of: 0.402526321278463
WordScorePoS
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Label: 8, with a score of: 0.35665445789323
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Label: 12, with a score of: 0.441315618113119
WordScorePoS
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Label: 1, with a score of: 0.415775172984061
WordScorePoS
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Label: 11, with a score of: 0.345056053006275
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Label: 10, with a score of: 0.33630583006815
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The base of the pyramid represents the foundation of basic services that all cities require

Label: 11, with a score of: 0.901526769154195
WordScorePoS
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The specifics are unique for any given city but in general the foundational building blocks shown here create an enabling environment that encourages and drives progress

Label: 11, with a score of: 0.866846611437291
WordScorePoS
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Label: 17, with a score of: 0.266263984054424
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After putting this foundation in place cities expand quality and coverage of service through greater efficiency and partnerships

Label: 11, with a score of: 0.801458152379294
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Label: 17, with a score of: 0.47550599356939
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Sustainable cites are supported through local and global connectivity and strong capacity for resilience disaster preparedness and proactive disaster risk reduction

Label: 11, with a score of: 0.716924068355458
WordScorePoS
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Our under standing of sustainable cities in this report is linked with this framework which aims to provide simple model for development path toward sustainable city

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The Urban Ecosystem Another way of understanding what makes city sustainable is through the analogy of the urban ecosystem

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WordScorePoS
urban0.27622591898666JJ
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Label: 15, with a score of: 0.226633989038287
WordScorePoS
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A biological ecosystem has been defined as community of living things interacting with nonliving things Chapin et al

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see also Box

Label: 0, with a score of: 1

In an urban ecosystem people are among the living things and the buildings streets and other built structures are among the nonliving things

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Label: 17, with a score of: 0.445178500522669
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Label: 15, with a score of: 0.275068336766276
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FIG

Label: 0, with a score of: 1

Sustainable City Hierarchy Model for Developing Sustainable City Environmental Security Economic Competitiveness and Social Inclusion and Equity Local and Global Connectivity Resilience Integrated Finance Gains Coverage and Reliability Public Participation Basic Service Provision Innovation in Science and Technology Improved Environmental Management Ecosystem Protection Credible Legal and Regulatory Framework Innovation in Investment and Financing Service Provison Incentives for Efficiency Reliable Governance and Institutions Connectivity Support to Diversity Active Private Sector Involvement Access to Innovation Knowledge Base Clear and Public Indicators Resilience to Disasters Active Risk Reduction Multi level Governance Coordination Defined Spatial Urban Form Service Master Plans Innovation in Istitutions and Policy Continuous Improvement Public Perception and Participation True Partnership Sufficient Land Supply and Physical Infrastructure Global Collaboration Leadership Strengthening Accountability and Oversight Local and Global Basic Services Water WW MSW Electricity Urban Transport Clear Performance Targets Community and Private Sector Inclusion Consideration of Service to Poor Source Henry Jewell World Bank BOX Ecosystems and Ecosystem Services Photo Curt Carnemark World Bank An ecosystem can be described as natural area that functions as unit consisting of components such as plants animals micro organisms water air etc

Label: 11, with a score of: 0.551675022406774
WordScorePoS
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reliable0JJ
governance0NN
institution0NN
support0.0224752180723703NN
diversity0NN
active0JJ
private0JJ
sector0NN
access0.00626002771693937NN
knowledge0NN
base0NN
disaster0.157957029085893NN
risk0.0631828116343571NN
reduction0.0268848280079943NN
multus0NN
level0.00692515656684925NN
coordination0NN
urban0.27622591898666JJ
form0NN
plan0.0583751976558017NN
policy0.0136980135278128NN
improvement0NN
partnership0NN
sufficient0JJ
land0.0389167984372011NN
supply0NN
infrastructure0.0194583992186006NN
leadership0NN
strengthen0.0179828714372571VB
accountability0NN
oversight0NN
water0.0458109463416786NN
electricity0NN
transport0.0747035830159326NN
performance0NN
target0NN
community0NN
poor0.0229054731708393JJ
source0NN
world0.00626002771693937NN
bank0NN
natural0.0229054731708393JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0.0373517915079663NN

Label: 17, with a score of: 0.361076250030245
WordScorePoS
sustainable0.0115176738164035JJ
city0NN
develop0VB
environmental0JJ
security0NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0.0130185595639838JJ
global0.0201198688839505JJ
resilience0NN
integrate0VB
finance0.036961556179878NN
gain0NN
coverage0NN
public0.0390556786919513NN
participation0NN
basic0JJ
service0NN
provision0NN
innovation0.0499801157438617NN
science0.0599173571845643NN
technology0.109843026967392NN
improve0.0183291958415182VB
management0NN
ecosystem0NN
protection0NN
credible0JJ
legal0JJ
framework0.0130185595639838NN
investment0.0766240489430275NN
financing0.036961556179878NN
incentive0.036961556179878NN
efficiency0NN
reliable0.0299586785922821JJ
governance0NN
institution0.0249900578719309NN
support0.0451108483130998NN
diversity0NN
active0.048933054487825JJ
private0.0898760357768464JJ
sector0.0459744293658165NN
access0.00314118376811005NN
knowledge0.0499801157438617NN
base0.017987180284335NN
disaster0NN
risk0NN
reduction0NN
multus0.0978661089756501NN
level0.0138997399903163NN
coordination0.0978661089756501NN
urban0JJ
form0NN
plan0.0130185595639838NN
policy0.0641521854453136NN
improvement0NN
partnership0.3914644359026NN
sufficient0JJ
land0NN
supply0NN
infrastructure0.0130185595639838NN
leadership0.036961556179878NN
strengthen0.0120313639527759VB
accountability0.048933054487825NN
oversight0.0978661089756501NN
water0NN
electricity0NN
transport0.0249900578719309NN
performance0NN
target0.0898760357768464NN
community0NN
poor0.0153248097886055JJ
source0.0109843026967392NN
world0.00418824502414673NN
bank0.036961556179878NN
natural0JJ
function0.036961556179878NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0NN

Label: 6, with a score of: 0.31762102812268
WordScorePoS
sustainable0.00146217101876223JJ
city0NN
develop0VB
environmental0JJ
security0.0348974218717993NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0.0181797964452808JJ
global0.00936547854413134JJ
resilience0NN
integrate0.0251182378961834VB
finance0NN
gain0.041835863322702NN
coverage0NN
public0NN
participation0.0348974218717993NN
basic0.0181797964452808JJ
service0.0084006124696649NN
provision0NN
innovation0NN
science0NN
technology0.0153390539205676NN
improve0.0511916925627245VB
management0.0428007293971684NN
ecosystem0.0295155485671997NN
protection0NN
credible0JJ
legal0JJ
framework0NN
investment0NN
financing0NN
incentive0NN
efficiency0.0502364757923669NN
reliable0JJ
governance0NN
institution0NN
support0.0209983814716066NN
diversity0NN
active0JJ
private0JJ
sector0.0214003646985842NN
access0.00731085509381114NN
knowledge0NN
base0NN
disaster0.0590310971343993NN
risk0NN
reduction0NN
multus0NN
level0.00647010760354177NN
coordination0NN
urban0JJ
form0NN
plan0NN
policy0NN
improvement0NN
partnership0NN
sufficient0.0516150472983178JJ
land0NN
supply0.0590310971343993NN
infrastructure0.0181797964452808NN
leadership0NN
strengthen0.0084006124696649VB
accountability0NN
oversight0NN
water0.556409482163189NN
electricity0.0348974218717993NN
transport0NN
performance0NN
target0NN
community0.0214003646985842NN
poor0.0642010940957526JJ
source0.0613562156822703NN
world0.00438651305628668NN
bank0NN
natural0.0214003646985842JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0NN

Label: 1, with a score of: 0.26350072117789
WordScorePoS
sustainable0.00514687440545096JJ
city0NN
develop0VB
environmental0.0442083771593746JJ
security0NN
economic0.0959899298575892JJ
competitiveness0NN
social0.112994637345193JJ
inclusion0NN
local0JJ
global0JJ
resilience0.0614198493690006NN
integrate0VB
finance0NN
gain0NN
coverage0.0908430554521382NN
public0NN
participation0.0614198493690006NN
basic0.0639932865717261JJ
service0.0443555132287109NN
provision0NN
innovation0NN
science0NN
technology0.0269969049497485NN
improve0VB
management0NN
ecosystem0NN
protection0.122839698738001NN
credible0JJ
legal0JJ
framework0.0319966432858631NN
investment0.0376648791150643NN
financing0NN
incentive0NN
efficiency0NN
reliable0JJ
governance0NN
institution0NN
support0.0184786920895884NN
diversity0NN
active0JJ
private0JJ
sector0NN
access0.00514687440545096NN
knowledge0NN
base0.0442083771593746NN
disaster0.0519476926891779NN
risk0.0519476926891779NN
reduction0NN
multus0NN
level0.0113874611101829NN
coordination0NN
urban0JJ
form0.0884167543187491NN
plan0NN
policy0.0450489732120807NN
improvement0NN
partnership0NN
sufficient0JJ
land0.0319966432858631NN
supply0NN
infrastructure0NN
leadership0NN
strengthen0VB
accountability0NN
oversight0NN
water0NN
electricity0NN
transport0NN
performance0NN
target0NN
community0NN
poor0.150659516460257JJ
source0.0269969049497485NN
world0.00257343720272548NN
bank0NN
natural0.0376648791150643JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0NN

Label: 9, with a score of: 0.248907622400603
WordScorePoS
sustainable0.00901749491165909JJ
city0NN
develop0VB
environmental0.0221298700466866JJ
security0NN
economic0.0320338181740375JJ
competitiveness0NN
social0.0188543197850406JJ
inclusion0NN
local0JJ
global0JJ
resilience0NN
integrate0VB
finance0NN
gain0NN
coverage0NN
public0.0160169090870187NN
participation0NN
basic0.0480507272610562JJ
service0.0148023490369527NN
provision0NN
innovation0.122982418460916NN
science0NN
technology0.0810848128688654NN
improve0VB
management0NN
ecosystem0NN
protection0NN
credible0JJ
legal0JJ
framework0NN
investment0.0377086395700813NN
financing0NN
incentive0NN
efficiency0.0442597400933733NN
reliable0.0737171311497939JJ
governance0NN
institution0NN
support0.0277502419802315NN
diversity0NN
active0JJ
private0.0368585655748969JJ
sector0.0565629593551219NN
access0.0128821355880844NN
knowledge0NN
base0NN
disaster0NN
risk0NN
reduction0NN
multus0NN
level0NN
coordination0NN
urban0JJ
form0NN
plan0NN
policy0.011275328195446NN
improvement0.0454743001434393NN
partnership0NN
sufficient0JJ
land0NN
supply0NN
infrastructure0.192202909044225NN
leadership0NN
strengthen0VB
accountability0NN
oversight0NN
water0.0377086395700813NN
electricity0.030745604615229NN
transport0.030745604615229NN
performance0NN
target0NN
community0.0188543197850406NN
poor0JJ
source0.0135141354781442NN
world0.00257642711761688NN
bank0NN
natural0JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0NN

Label: 12, with a score of: 0.233706416079308
WordScorePoS
sustainable0.0162522423871414JJ
city0NN
develop0VB
environmental0.0598270388037045JJ
security0NN
economic0.0433009429988926JJ
competitiveness0.0542519672965169NN
social0.0169905820958638JJ
inclusion0NN
local0.0144336476662975JJ
global0.022306853314744JJ
resilience0NN
integrate0.0199423462679015VB
finance0NN
gain0.0664302389559493NN
coverage0NN
public0.028867295332595NN
participation0NN
basic0.0144336476662975JJ
service0.00666957305782837NN
provision0NN
innovation0NN
science0NN
technology0NN
improve0VB
management0.0339811641917276NN
ecosystem0.0234335422829798NN
protection0NN
credible0JJ
legal0JJ
framework0.028867295332595NN
investment0NN
financing0NN
incentive0NN
efficiency0.039884692535803NN
reliable0JJ
governance0NN
institution0NN
support0.00833571598658786NN
diversity0NN
active0JJ
private0JJ
sector0.0169905820958638NN
access0.00232174891244877NN
knowledge0NN
base0.0199423462679015NN
disaster0NN
risk0NN
reduction0.0199423462679015NN
multus0NN
level0.00513687014008325NN
coordination0NN
urban0JJ
form0NN
plan0.0144336476662975NN
policy0.0203215381458133NN
improvement0NN
partnership0NN
sufficient0JJ
land0.0144336476662975NN
supply0.0703006268489394NN
infrastructure0.028867295332595NN
leadership0NN
strengthen0.0133391461156567VB
accountability0NN
oversight0NN
water0.186896403054502NN
electricity0NN
transport0.0277064208763707NN
performance0NN
target0NN
community0.0169905820958638NN
poor0.0339811641917276JJ
source0NN
world0.00464349782489754NN
bank0NN
natural0.0509717462875914JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0.0554128417527413NN

Label: 15, with a score of: 0.221423079419088
WordScorePoS
sustainable0.00664197120025784JJ
city0NN
develop0VB
environmental0JJ
security0.0264204925485796NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0.0275274877486226JJ
global0.00354525553489471JJ
resilience0NN
integrate0.0190167691930804VB
finance0.0390772412228479NN
gain0NN
coverage0NN
public0NN
participation0NN
basic0.0137637438743113JJ
service0.0127200410376242NN
provision0NN
innovation0NN
science0NN
technology0NN
improve0VB
management0.0486060126274891NN
ecosystem0.134075577362426NN
protection0NN
credible0JJ
legal0JJ
framework0NN
investment0NN
financing0NN
incentive0.0390772412228479NN
efficiency0NN
reliable0JJ
governance0NN
institution0NN
support0.00794883334420673NN
diversity0NN
active0JJ
private0JJ
sector0NN
access0.00110699520004297NN
knowledge0NN
base0.0190167691930804NN
disaster0NN
risk0.0223459295604043NN
reduction0.0190167691930804NN
multus0NN
level0.00489845440032415NN
coordination0NN
urban0JJ
form0NN
plan0.0137637438743113NN
policy0NN
improvement0NN
partnership0NN
sufficient0JJ
land0.123873694868802NN
supply0.0223459295604043NN
infrastructure0NN
leadership0NN
strengthen0VB
accountability0NN
oversight0NN
water0.016202004209163NN
electricity0NN
transport0NN
performance0NN
target0NN
community0.016202004209163NN
poor0.016202004209163JJ
source0.0232260916751625NN
world0.00110699520004297NN
bank0NN
natural0.016202004209163JJ
function0NN
plant0.117231723668544NN
animal0.0950205536020462NN
micro0.0390772412228479JJ
organism0.0517339898971162NN
air0NN

Label: 2, with a score of: 0.215649474754122
WordScorePoS
sustainable0.00374515576585943JJ
city0.0440683498519077NN
develop0VB
environmental0JJ
security0.0893850849591018NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0.0155217067874935JJ
global0.0119942111830662JJ
resilience0NN
integrate0VB
finance0NN
gain0NN
coverage0NN
public0NN
participation0NN
basic0JJ
service0.0143446978608332NN
provision0NN
innovation0NN
science0NN
technology0.0130963126055467NN
improve0.0218534471578307VB
management0NN
ecosystem0.0252000451111224NN
protection0NN
credible0JJ
legal0JJ
framework0NN
investment0.0182713919265625NN
financing0NN
incentive0NN
efficiency0NN
reliable0JJ
governance0NN
institution0NN
support0.00896409157262106NN
diversity0.0881366997038154NN
active0JJ
private0JJ
sector0.0182713919265625NN
access0.00749031153171887NN
knowledge0.0595900566394012NN
base0NN
disaster0.0504000902222448NN
risk0.0252000451111224NN
reduction0NN
multus0NN
level0.00552410547653725NN
coordination0NN
urban0JJ
form0.0428913409252473NN
plan0NN
policy0NN
improvement0NN
partnership0NN
sufficient0.0440683498519077JJ
land0.0465651203624806NN
supply0NN
infrastructure0.0155217067874935NN
leadership0NN
strengthen0.00717234893041658VB
accountability0NN
oversight0NN
water0NN
electricity0.0297950283197006NN
transport0NN
performance0NN
target0.0357189919948307NN
community0.0182713919265625NN
poor0.0548141757796873JJ
source0.0130963126055467NN
world0.0124838525528648NN
bank0.0881366997038154NN
natural0JJ
function0.0440683498519077NN
plant0.132205049555723NN
animal0.0357189919948307NN
micro0JJ
organism0NN
air0NN

Label: 7, with a score of: 0.192360827690862
WordScorePoS
sustainable0.0104774661535722JJ
city0NN
develop0VB
environmental0JJ
security0.0625160381065121NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0JJ
global0.0251662980592747JJ
resilience0NN
integrate0VB
finance0NN
gain0NN
coverage0NN
public0NN
participation0NN
basic0JJ
service0.0451471467893167NN
provision0NN
innovation0NN
science0NN
technology0.109914925284606NN
improve0.0229264916384433VB
management0NN
ecosystem0NN
protection0NN
credible0JJ
legal0JJ
framework0NN
investment0.0383371018038178NN
financing0NN
incentive0NN
efficiency0.134992154141495NN
reliable0.149891440995783JJ
governance0NN
institution0NN
support0.0188084899376888NN
diversity0NN
active0JJ
private0JJ
sector0NN
access0.0157161992303584NN
knowledge0NN
base0NN
disaster0NN
risk0NN
reduction0NN
multus0NN
level0NN
coordination0NN
urban0JJ
form0NN
plan0NN
policy0NN
improvement0.0924643738905717NN
partnership0NN
sufficient0JJ
land0.0325677023224526NN
supply0.0528748274225028NN
infrastructure0.0651354046449052NN
leadership0NN
strengthen0VB
accountability0NN
oversight0NN
water0NN
electricity0.0625160381065121NN
transport0NN
performance0NN
target0NN
community0NN
poor0JJ
source0.0274787313211514NN
world0.00261936653839306NN
bank0NN
natural0JJ
function0NN
plant0NN
animal0.0749457204978913NN
micro0JJ
organism0NN
air0NN

Label: 8, with a score of: 0.187392961990116
WordScorePoS
sustainable0.00688808874408283JJ
city0NN
develop0VB
environmental0.0295821503613099JJ
security0NN
economic0.128463821595706JJ
competitiveness0NN
social0.0504071033101074JJ
inclusion0NN
local0.0214106369326177JJ
global0.0220597476338268JJ
resilience0NN
integrate0.0295821503613099VB
finance0NN
gain0NN
coverage0NN
public0NN
participation0NN
basic0.0214106369326177JJ
service0.0197870712294258NN
provision0NN
innovation0.0821985033584295NN
science0NN
technology0NN
improve0.0150723186962745VB
management0NN
ecosystem0NN
protection0NN
credible0JJ
legal0JJ
framework0.0428212738652355NN
investment0.0252035516550537NN
financing0NN
incentive0NN
efficiency0.0295821503613099NN
reliable0JJ
governance0NN
institution0.0410992516792148NN
support0.024730129581725NN
diversity0NN
active0JJ
private0JJ
sector0.0252035516550537NN
access0.00344404437204141NN
knowledge0NN
base0NN
disaster0NN
risk0NN
reduction0NN
multus0NN
level0.00761994916892276NN
coordination0NN
urban0JJ
form0.0591643007226199NN
plan0NN
policy0.0452169560888236NN
improvement0NN
partnership0NN
sufficient0JJ
land0NN
supply0NN
infrastructure0NN
leadership0NN
strengthen0.00989353561471291VB
accountability0NN
oversight0NN
water0NN
electricity0NN
transport0NN
performance0NN
target0NN
community0NN
poor0JJ
source0NN
world0.00172202218602071NN
bank0NN
natural0JJ
function0NN
plant0NN
animal0NN
micro0.0607878664258118JJ
organism0NN
air0NN

Label: 16, with a score of: 0.167072949585836
WordScorePoS
sustainable0.00920028227269801JJ
city0NN
develop0VB
environmental0JJ
security0NN
economic0JJ
competitiveness0NN
social0JJ
inclusion0NN
local0JJ
global0.00736619084902312JJ
resilience0NN
integrate0VB
finance0NN
gain0NN
coverage0NN
public0.0285977592242194NN
participation0.0548954478802642NN
basic0JJ
service0NN
provision0.0658099748168827NN
innovation0NN
science0NN
technology0NN
improve0VB
management0NN
ecosystem0NN
protection0NN
credible0JJ
legal0.0811931365363091JJ
framework0NN
investment0NN
financing0NN
incentive0NN
efficiency0NN
reliable0JJ
governance0.107490825192354NN
institution0.274477239401321NN
support0NN
diversity0NN
active0JJ
private0JJ
sector0NN
access0.00690021170452351NN
knowledge0NN
base0NN
disaster0NN
risk0NN
reduction0NN
multus0NN
level0.0610668815653026NN
coordination0NN
urban0JJ
form0.158049144643351NN
plan0NN
policy0.0201317944152378NN
improvement0NN
partnership0NN
sufficient0JJ
land0NN
supply0NN
infrastructure0NN
leadership0NN
strengthen0.0396437925143789VB
accountability0NN
oversight0NN
water0NN
electricity0NN
transport0NN
performance0NN
target0NN
community0NN
poor0JJ
source0NN
world0NN
bank0NN
natural0JJ
function0NN
plant0NN
animal0NN
micro0JJ
organism0NN
air0NN

and the interactions between them

Label: 0, with a score of: 1

Functioning ecosystems are the foundation of human wellbeing and most economic activity because almost every resource that humankind utilizes on day to day basis relies directly or indirectly on nature

Label: 1, with a score of: 0.723057259467214
WordScorePoS
function0NN
ecosystem0NN
foundation0NN
human0JJ
economic0.0959899298575892JJ
activity0NN
resource0.0739147683583536NN
humankind0NN
utilize0VB
day0.25973846344589NN
basis0NN
rely0VB
indirectly0RB
nature0NN

Label: 12, with a score of: 0.350826582463109
WordScorePoS
function0NN
ecosystem0.0234335422829798NN
foundation0NN
human0.0101607690729067JJ
economic0.0433009429988926JJ
activity0.0199423462679015NN
resource0.0416785799329393NN
humankind0NN
utilize0VB
day0NN
basis0NN
rely0.0332151194779746VB
indirectly0RB
nature0.162755901889551NN

Label: 8, with a score of: 0.267461984033693
WordScorePoS
function0NN
ecosystem0NN
foundation0NN
human0.0150723186962745JJ
economic0.128463821595706JJ
activity0.0295821503613099NN
resource0.0123650647908625NN
humankind0NN
utilize0VB
day0.0347609334428716NN
basis0NN
rely0VB
indirectly0RB
nature0NN

Label: 15, with a score of: 0.247464981848538
WordScorePoS
function0NN
ecosystem0.134075577362426NN
foundation0NN
human0.0193783617722719JJ
economic0JJ
activity0.0190167691930804NN
resource0.0317953333768269NN
humankind0NN
utilize0VB
day0NN
basis0NN
rely0.0316735178673487VB
indirectly0RB
nature0NN

The benefits that humans derive from nature are known as ecosystem services which can be divided into four categories provisioning services what we consume directly regulating services what protects us from extreme events cultural services natural systems that we use for recreation religious or spiritual purposes and supporting services the underlying processes that deliver the other services

Label: 1, with a score of: 0.624023381536569
WordScorePoS
human0JJ
nature0NN
ecosystem0NN
service0.0443555132287109NN
provision0NN
consume0VB
regulate0VB
protect0VB
extreme0.294526332970048JJ
event0.0908430554521382NN
cultural0JJ
natural0.0376648791150643JJ
system0.0519476926891779NN
support0.0184786920895884NN
underlie0VB
process0NN
deliver0VB

Label: 12, with a score of: 0.313860801042677
WordScorePoS
human0.0101607690729067JJ
nature0.162755901889551NN
ecosystem0.0234335422829798NN
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WordScorePoS
human0.0193783617722719JJ
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Ecosystem management has long been recognized as key component to sustainable development and poverty alleviation with the use of sustainable resource management in urban and peri urban areas shown to provide livelihoods for communities throughout the world

Label: 11, with a score of: 0.700643226514071
WordScorePoS
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WordScorePoS
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Label: 1, with a score of: 0.360714519380123
WordScorePoS
ecosystem0NN
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Source Reprinted from Morcotullio and Boyle

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WordScorePoS
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The Urban Long Term Research Area ULTRA program in the United States is now studying the ecological flows and interactions in cities

Label: 11, with a score of: 0.949656335862602
WordScorePoS
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WordScorePoS
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Researchers at Boston University for example have discovered weekend effect on emissions steep dropoff in the amount of carbon dioxide entering the city atmosphere on Saturdays and Sundays

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WordScorePoS
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WordScorePoS
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In Fresno California backyard water use increases with wealth as does backyard biodiversity

Label: 6, with a score of: 0.847802959146642
WordScorePoS
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Label: 15, with a score of: 0.366988263712711
WordScorePoS
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Label: 12, with a score of: 0.294587315022976
WordScorePoS
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In Los Angeles ecologists studying the city ecohydrology have calculated that planting million new trees an idea with fairly universal appeal would have the drawback of increasing water consumption by percent

Label: 11, with a score of: 0.693392550871426
WordScorePoS
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WordScorePoS
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WordScorePoS
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Label: 8, with a score of: 0.128518302207141
WordScorePoS
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In recent years an ecosystem approach has become more widely used in city management

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WordScorePoS
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Label: 15, with a score of: 0.253532375500992
WordScorePoS
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Adopted by the UN Convention on Biological Diversity CBD Ibisch Vega and Hermann Smith and Maltby the ecosystem approach is strategy for the integrated management of land water and living resources that promotes conservation and sustainable use in an equitable way

Label: 6, with a score of: 0.61082135208102
WordScorePoS
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Label: 15, with a score of: 0.455264520962032
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Label: 12, with a score of: 0.348351782985141
WordScorePoS
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The rise of this type of strategy is due in part to the Millennium Ecosystem Assessment MA which concluded that human impacts on the health and biodiversity of world ecosystems are significant and escalating

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WordScorePoS
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In the wake of this report variety of innovations breakthroughs in the under BUILDING SUSTAINABILITY IN AN URBANIZING WORLD standing of ecosystem dynamics green paradigms in economics Boxes and and in building design and new financial mechanisms have allowed for more policymakers including urban managers to consider taking an ecosystem approach

Label: 17, with a score of: 0.595426547685943
WordScorePoS
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Label: 11, with a score of: 0.567985769047391
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Label: 15, with a score of: 0.297525130788609
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Urban management using the ecosystem approach recognizes that city is component of one or more ecosystems and thus city managers must consider variables beyond the city borders when defining and implementing policies see Box

Label: 11, with a score of: 0.973383701793101
WordScorePoS
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Label: 15, with a score of: 0.139569251610749
WordScorePoS
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Ecosystem thinking can bring broad benefits across the three pillars of sustainability for example by highlighting the value of natural capital and the dependence of poor populations on well functioning ecosystems MA it helps cities balance socioeconomic concerns with environmental protection

Label: 11, with a score of: 0.763989576620256
WordScorePoS
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Label: 1, with a score of: 0.339429285809509
WordScorePoS
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Label: 15, with a score of: 0.300217520178876
WordScorePoS
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Economic Valuation of Ecosystem Services BOX The economic valuation of ecosystem services is an emerging science that has seen experts attempt to quantify the contribution of such services to the local or national economy

Label: 8, with a score of: 0.484174427710692
WordScorePoS
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Label: 15, with a score of: 0.420034767205391
WordScorePoS
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While the process is challenging it allows policy makers to propose policies relating to the natural environment that can be weighed against other competing activities such as largescale infrastructure development

Label: 9, with a score of: 0.492008125824572
WordScorePoS
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Label: 12, with a score of: 0.405914305875579
WordScorePoS
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Label: 8, with a score of: 0.314238948824814
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For instance in Chicago USA urban trees are estimated to provide service for air cleansing that is equivalent to US

Label: 11, with a score of: 0.786389508053809
WordScorePoS
urban0.27622591898666JJ
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Label: 12, with a score of: 0.360458808882889
WordScorePoS
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million dollars and their long term benefits are estimated to be more than twice their costs

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Label: 10, with a score of: 0.405112607316771
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Source Reprinted from MacPherson et al

Label: 6, with a score of: 0.73415387760794
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Photo Bigstock Economic considerations have helped in the development of payments for ecosystem services PES the practice of offering transparent voluntary incentives to landowners in exchange for managing their land to provide some sort of ecological service

Label: 15, with a score of: 0.560328113133891
WordScorePoS
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Label: 8, with a score of: 0.225911306277313
WordScorePoS
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Label: 12, with a score of: 0.340228645206704
WordScorePoS
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PES programs promote the conservation of natural resources in the marketplace

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Label: 14, with a score of: 0.450866551269206
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Label: 1, with a score of: 0.392659477961591
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Label: 12, with a score of: 0.352352654216189
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The ecosystem services that these schemes usually focus on are climate change mitigation watershed services and biodiversity conservation all of which are subject to growing demand

Label: 13, with a score of: 0.702693655466401
WordScorePoS
ecosystem0NN
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Label: 15, with a score of: 0.52817624369186
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Some PES schemes involve contracts between consumers of ecosystem BOX Payments for Ecosystem Services services and the suppliers of these services but the majority are funded by governments and also involve intermediaries such as NGOs

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WordScorePoS
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Label: 15, with a score of: 0.402140523183817
WordScorePoS
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The party supplying the environmental services normally holds the property rights over an environmental good which provides flow of benefits to the demanding party in return for compensation

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WordScorePoS
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In the case of private contracts the beneficiaries are willing to pay price that can be expected to be lower than their gain in welfare due to the services

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WordScorePoS
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Label: 8, with a score of: 0.424131969740403
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Label: 5, with a score of: 0.392183592075313
WordScorePoS
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Label: 6, with a score of: 0.346686770471486
WordScorePoS
private0JJ
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The providers of the ecosystem services can be expected to be willing to accept payment that is greater than the cost of providing the services

Label: 15, with a score of: 0.577579926373073
WordScorePoS
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Label: 12, with a score of: 0.333190836973611
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Label: 2, with a score of: 0.314571488782391
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Photo Curt Carnemark World Bank BOX Case Study Urban Freshwater Resources in Los Angeles In Los Angeles California obtained all of its water from the Los Angeles River but population growth primarily due to in migration caused the city needs to exceed this local water supply early in the th century

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Label: 11, with a score of: 0.509094750958915
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migration0NN
cause0.04477822029736NN
city0.607697021770652NN
exceed0VB
local0.0194583992186006JJ
supply0NN
century0NN

Label: 12, with a score of: 0.267599848918536
WordScorePoS
world0.00464349782489754NN
bank0NN
study0NN
urban0JJ
freshwater0NN
resource0.0416785799329393NN
obtain0VB
water0.186896403054502NN
river0.0409791940864438NN
population0.0144336476662975NN
growth0NN
primarily0RB
due0.0234335422829798JJ
migration0NN
cause0NN
city0NN
exceed0VB
local0.0144336476662975JJ
supply0.0703006268489394NN
century0NN

Label: 10, with a score of: 0.173700986606055
WordScorePoS
world0.00276879546803864NN
bank0NN
study0NN
urban0.0488696285210061JJ
freshwater0NN
resource0NN
obtain0VB
water0NN
river0NN
population0.0860640399817412NN
growth0.11178243319132NN
primarily0RB
due0JJ
migration0.12939607756667NN
cause0NN
city0NN
exceed0VB
local0JJ
supply0NN
century0NN

Label: 13, with a score of: 0.168520122347949
WordScorePoS
world0.00351929966767313NN
bank0NN
study0NN
urban0JJ
freshwater0NN
resource0NN
obtain0VB
water0NN
river0NN
population0NN
growth0NN
primarily0RB
due0.0473606524958885JJ
migration0NN
cause0NN
city0NN
exceed0.0828215106044499VB
local0.0145856516935579JJ
supply0NN
century0.219293228426234NN

Label: 8, with a score of: 0.131524518551999
WordScorePoS
world0.00172202218602071NN
bank0NN
study0NN
urban0JJ
freshwater0NN
resource0.0123650647908625NN
obtain0VB
water0NN
river0NN
population0.0428212738652355NN
growth0.208565600657229NN
primarily0RB
due0JJ
migration0NN
cause0NN
city0NN
exceed0VB
local0.0214106369326177JJ
supply0NN
century0NN

The system was then extended to other water basins up to hundreds of kilometers away

Label: 6, with a score of: 0.930700765374237
WordScorePoS
system0NN
extend0VB
water0.556409482163189NN
basin0.0683326727248364NN
hundred0CD

Label: 12, with a score of: 0.278426265952381
WordScorePoS
system0NN
extend0VB
water0.186896403054502NN
basin0NN
hundred0CD

The ecological impacts of this expanding watersupply system have been serious and widespread

Label: 12, with a score of: 0.44580359987832
WordScorePoS
impact0.0866018859977851NN
expand0VB
system0NN

Label: 11, with a score of: 0.401186874463665
WordScorePoS
impact0.0194583992186006NN
expand0.0268848280079943VB
system0.0315914058171786NN

Label: 2, with a score of: 0.389169497807071
WordScorePoS
impact0NN
expand0VB
system0.0756001353333673NN

Label: 14, with a score of: 0.336859575887978
WordScorePoS
impact0.042459973210532NN
expand0VB
system0.0229784274580034NN

Label: 13, with a score of: 0.328988266585085
WordScorePoS
impact0.0437569550806737NN
expand0.0201523636533764VB
system0NN

By the mid th century the natural Los Angeles River ecosystem had become severely degraded by combination of agricultural and municipal water use water pollution and flood control structures

Label: 6, with a score of: 0.873979776613146
WordScorePoS
century0NN
natural0.0214003646985842JJ
river0.206460189193271NN
ecosystem0.0295155485671997NN
severely0RB
degrade0VB
agricultural0JJ
municipal0JJ
water0.556409482163189NN
pollution0.0590310971343993NN
control0NN
structure0NN

Label: 12, with a score of: 0.313464384377735
WordScorePoS
century0NN
natural0.0509717462875914JJ
river0.0409791940864438NN
ecosystem0.0234335422829798NN
severely0RB
degrade0VB
agricultural0JJ
municipal0JJ
water0.186896403054502NN
pollution0.0234335422829798NN
control0NN
structure0NN

Label: 2, with a score of: 0.195254550967775
WordScorePoS
century0NN
natural0JJ
river0NN
ecosystem0.0252000451111224NN
severely0RB
degrade0.0440683498519077VB
agricultural0.250032943963815JJ
municipal0JJ
water0NN
pollution0NN
control0NN
structure0NN

Label: 15, with a score of: 0.175658992840565
WordScorePoS
century0NN
natural0.016202004209163JJ
river0NN
ecosystem0.134075577362426NN
severely0RB
degrade0.0781544824456958VB
agricultural0JJ
municipal0JJ
water0.016202004209163NN
pollution0NN
control0.0264204925485796NN
structure0NN

Label: 13, with a score of: 0.144598286701581
WordScorePoS
century0.219293228426234NN
natural0.0171695137813101JJ
river0NN
ecosystem0NN
severely0RB
degrade0VB
agricultural0JJ
municipal0JJ
water0NN
pollution0NN
control0NN
structure0NN

Reduced freshwater inflows have seriously degraded the wetlands and once productive fisheries while other effects included the creation of dust storms that affected local residents

Label: 8, with a score of: 0.611656653498321
WordScorePoS
reduce0.0055149369084567VB
freshwater0NN
degrade0VB
wetland0NN
productive0.0985415302158139JJ
effect0NN
include0VB
creation0.160952962344818NN
affect0VB
local0.0214106369326177JJ

Label: 15, with a score of: 0.476792693900916
WordScorePoS
reduce0.00709051106978941VB
freshwater0.0316735178673487NN
degrade0.0781544824456958VB
wetland0.0390772412228479NN
productive0JJ
effect0NN
include0VB
creation0NN
affect0.0397441667210336VB
local0.0275274877486226JJ

Label: 2, with a score of: 0.467277051319209
WordScorePoS
reduce0.00799614078871077VB
freshwater0.0357189919948307NN
degrade0.0440683498519077VB
wetland0NN
productive0.0714379839896614JJ
effect0.0440683498519077NN
include0VB
creation0NN
affect0VB
local0.0155217067874935JJ

series of lawsuits throughout the and forced the urban authorities to restore flows and wildlife habitats mitigate dust storms and limit water exports to allow lake elevations to return to more natural levels

Label: 6, with a score of: 0.769525828412663
WordScorePoS
force0NN
urban0JJ
restore0.041835863322702VB
wildlife0NN
habitat0NN
limit0NN
water0.556409482163189NN
export0NN
lake0.103230094596636NN
return0NN
natural0.0214003646985842JJ
level0.00647010760354177NN

Label: 11, with a score of: 0.371251995842228
WordScorePoS
force0NN
urban0.27622591898666JJ
restore0VB
wildlife0NN
habitat0NN
limit0NN
water0.0458109463416786NN
export0NN
lake0NN
return0NN
natural0.0229054731708393JJ
level0.00692515656684925NN

Label: 12, with a score of: 0.299629001125131
WordScorePoS
force0NN
urban0JJ
restore0VB
wildlife0NN
habitat0NN
limit0NN
water0.186896403054502NN
export0NN
lake0.0409791940864438NN
return0NN
natural0.0509717462875914JJ
level0.00513687014008325NN

These events marked the beginning of transition in Los Angeles water supply sources and water demand

Label: 6, with a score of: 0.920268608870489
WordScorePoS
event0NN
water0.556409482163189NN
supply0.0590310971343993NN
source0.0613562156822703NN
demand0NN

Label: 12, with a score of: 0.331400555877417
WordScorePoS
event0NN
water0.186896403054502NN
supply0.0703006268489394NN
source0NN
demand0NN

Following lengthy drought from through Los Angeles began to invest seriously in reducing water demand as result per capita water usage decreased by percent between and

Label: 6, with a score of: 0.909849385448041
WordScorePoS
drought0.041835863322702NN
invest0VB
reduce0.00936547854413134VB
water0.556409482163189NN
demand0NN
result0.0683326727248364NN
capita0NN
decrease0NN

Label: 12, with a score of: 0.316964209467351
WordScorePoS
drought0NN
invest0VB
reduce0.022306853314744VB
water0.186896403054502NN
demand0NN
result0NN
capita0.0332151194779746NN
decrease0NN

The city population is projected to grow from

Label: 11, with a score of: 0.945321900946976
WordScorePoS
city0.607697021770652NN
population0.0194583992186006NN
project0NN
grow0.0229054731708393VB

million people in and future increases in demand are to be met through water conservation and recycling

Label: 6, with a score of: 0.817314953403678
WordScorePoS
people0.024149913320522NNS
future0NN
increase0.00905621749519576NN
demand0NN
meet0VB
water0.556409482163189NN
conservation0NN
recycle0.103230094596636VB

Label: 12, with a score of: 0.37528355654032
WordScorePoS
people0.0119834723221875NNS
future0.0277064208763707NN
increase0.00958677785775002NN
demand0NN
meet0VB
water0.186896403054502NN
conservation0NN
recycle0.0819583881728876VB

Resource efficiency the sustainable use of resources throughout their life cycle including extraction transport consumption and waste disposal is often the primary goal for officials exploring an integrated approach to city management

Label: 11, with a score of: 0.727998568802722
WordScorePoS
resource0.0449504361447405NN
sustainable0.00782503464617421JJ
life0.0112376090361851NN
cycle0NN
include0VB
transport0.0747035830159326NN
consumption0.08955644059472NN
waste0.0373517915079663NN
primary0JJ
goal0NN
integrate0.0537696560159885VB
approach0NN
city0.607697021770652NN
management0.0687164195125178NN

Label: 12, with a score of: 0.587350492200911
WordScorePoS
resource0.0416785799329393NN
sustainable0.0162522423871414JJ
life0.0250071479597636NN
cycle0.108503934593034NN
include0VB
transport0.0277064208763707NN
consumption0.332151194779746NN
waste0.138532104381853NN
primary0JJ
goal0NN
integrate0.0199423462679015VB
approach0.0542519672965169NN
city0NN
management0.0339811641917276NN

Label: 8, with a score of: 0.154851179040467
WordScorePoS
resource0.0123650647908625NN
sustainable0.00688808874408283JJ
life0.0123650647908625NN
cycle0NN
include0VB
transport0NN
consumption0.147812295323721NN
waste0NN
primary0JJ
goal0NN
integrate0.0295821503613099VB
approach0NN
city0NN
management0NN

There is strong link between natural resource management and well being in cities

Label: 11, with a score of: 0.93933025024316
WordScorePoS
strong0JJ
natural0.0229054731708393JJ
resource0.0449504361447405NN
management0.0687164195125178NN
city0.607697021770652NN

Resource efficient cities combine greater productivity and innovation with lower costs and reduced environmental impact

Label: 11, with a score of: 0.851479318395999
WordScorePoS
resource0.0449504361447405NN
city0.607697021770652NN
productivity0.0373517915079663NN
innovation0.0373517915079663NN
cost0NN
reduce0.0200483235258943VB
environmental0.0537696560159885JJ
impact0.0194583992186006NN

neglect of natural resources can have dire consequences

Label: 1, with a score of: 0.553880903945311
WordScorePoS
neglect0NN
natural0.0376648791150643JJ
resource0.0739147683583536NN

Label: 12, with a score of: 0.459915832321312
WordScorePoS
neglect0NN
natural0.0509717462875914JJ
resource0.0416785799329393NN

Label: 11, with a score of: 0.336836450381314
WordScorePoS
neglect0NN
natural0.0229054731708393JJ
resource0.0449504361447405NN

Ulaanbaatar Mongolia depends on the watershed of the Upper Tuul valley which is rapidly degrading

Label: 15, with a score of: 0.808488668545597
WordScorePoS
depend0.0633470357346975VB
rapidly0RB
degrade0.0781544824456958VB

The reduced availability of water and other ecosystem services business asusual management and increasing degradation will result in an estimated cost of million to industry and reduced economic growth prospects for the city over years

Label: 11, with a score of: 0.531249057123493
WordScorePoS
reduce0.0200483235258943VB
availability0NN
water0.0458109463416786NN
ecosystem0NN
service0.0269743071558856NN
business0NN
management0.0687164195125178NN
increase0.00323105002784081NN
degradation0NN
result0NN
estimate0NN
cost0NN
industry0NN
economic0.0389167984372011JJ
growth0NN
prospects0NNS
city0.607697021770652NN

Label: 6, with a score of: 0.46850628823451
WordScorePoS
reduce0.00936547854413134VB
availability0NN
water0.556409482163189NN
ecosystem0.0295155485671997NN
service0.0084006124696649NN
business0NN
management0.0428007293971684NN
increase0.00905621749519576NN
degradation0NN
result0.0683326727248364NN
estimate0NN
cost0NN
industry0NN
economic0JJ
growth0NN
prospects0NNS
city0NN

Label: 12, with a score of: 0.342958868725683
WordScorePoS
reduce0.022306853314744VB
availability0NN
water0.186896403054502NN
ecosystem0.0234335422829798NN
service0.00666957305782837NN
business0.0409791940864438NN
management0.0339811641917276NN
increase0.00958677785775002NN
degradation0.0996453584339239NN
result0NN
estimate0.0199423462679015NN
cost0.0277064208763707NN
industry0NN
economic0.0433009429988926JJ
growth0NN
prospects0NNS
city0NN

Label: 9, with a score of: 0.307771759187014
WordScorePoS
reduce0VB
availability0NN
water0.0377086395700813NN
ecosystem0NN
service0.0148023490369527NN
business0.0909486002868787NN
management0NN
increase0.0159575617614215NN
degradation0NN
result0NN
estimate0.0221298700466866NN
cost0NN
industry0.181897200573757NN
economic0.0320338181740375JJ
growth0.0260040237236563NN
prospects0.0602029956716497NNS
city0NN

Label: 15, with a score of: 0.302460416991195
WordScorePoS
reduce0.00709051106978941VB
availability0NN
water0.016202004209163NN
ecosystem0.134075577362426NN
service0.0127200410376242NN
business0NN
management0.0486060126274891NN
increase0.0068563725919103NN
degradation0.221714625071441NN
result0NN
estimate0.0190167691930804NN
cost0NN
industry0NN
economic0JJ
growth0NN
prospects0NNS
city0NN

Label: 8, with a score of: 0.271059403326124
WordScorePoS
reduce0.0055149369084567VB
availability0NN
water0NN
ecosystem0NN
service0.0197870712294258NN
business0NN
management0NN
increase0.00711043475673939NN
degradation0.049270765107907NN
result0NN
estimate0NN
cost0NN
industry0NN
economic0.128463821595706JJ
growth0.208565600657229NN
prospects0NNS
city0NN

With increased pressure on natural resources city policies need to maintain and capitalize on those resources

Label: 11, with a score of: 0.870956834542973
WordScorePoS
increase0.00323105002784081NN
pressure0.055245183797332NN
natural0.0229054731708393JJ
resource0.0449504361447405NN
city0.607697021770652NN
policy0.0136980135278128NN
maintain0.055245183797332VB

For instance in Melbourne Australia network of regional parks trails foreshores and waterways contribute significantly to the city livability and public health

Label: 11, with a score of: 0.901112665286751
WordScorePoS
regional0.0315914058171786JJ
contribute0VB
city0.607697021770652NN
public0.0583751976558017NN
health0.0229054731708393NN

Label: 3, with a score of: 0.266450917598697
WordScorePoS
regional0JJ
contribute0VB
city0NN
public0.021678073222951NN
health0.191387734041787NN

Local park agencies have partnered with major health insurer and invested over US million in program for health care professionals to encourage people to increase physical activity by visiting and engaging in activities in these areas TEEB

Label: 3, with a score of: 0.658949152200896
WordScorePoS
local0JJ
agency0NN
major0.0108390366114755JJ
health0.191387734041787NN
invest0VB
care0.104031537001265NN
encourage0VB
people0.00539943740774902NNS
increase0.0143984997539974NN
activity0NN
engage0VB

Label: 12, with a score of: 0.416333310782131
WordScorePoS
local0.0144336476662975JJ
agency0.0542519672965169NN
major0JJ
health0.0339811641917276NN
invest0VB
care0NN
encourage0.0468670845659596VB
people0.0119834723221875NNS
increase0.00958677785775002NN
activity0.0199423462679015NN
engage0.0819583881728876VB

In contrast Source Reprinted from Fitzhugh and Richter

Label: 6, with a score of: 0.73415387760794
WordScorePoS
source0.0613562156822703NN

How Can Cities Be Made More Sustainable

Label: 11, with a score of: 0.995778113359572
WordScorePoS
city0.607697021770652NN
sustainable0.00782503464617421JJ

The complexity of urban systems and the close links between economic social and environmental objectives raise challenges in designing good urban policies as trade offs are inevitable

Label: 11, with a score of: 0.720994590039997
WordScorePoS
urban0.27622591898666JJ
system0.0315914058171786NN
economic0.0389167984372011JJ
social0.0458109463416786JJ
environmental0.0537696560159885JJ
objective0NN
raise0NN
challenge0.0947742174515357NN
policy0.0136980135278128NN
trade0NN
inevitable0JJ

Label: 8, with a score of: 0.301576922973869
WordScorePoS
urban0JJ
system0NN
economic0.128463821595706JJ
social0.0504071033101074JJ
environmental0.0295821503613099JJ
objective0NN
raise0NN
challenge0.0347609334428716NN
policy0.0452169560888236NN
trade0.0591643007226199NN
inevitable0JJ

Label: 10, with a score of: 0.433292190646436
WordScorePoS
urban0.0488696285210061JJ
system0NN
economic0.103276847978089JJ
social0.101310366625194JJ
environmental0.0237821918091848JJ
objective0NN
raise0NN
challenge0NN
policy0.0848203862485078NN
trade0.0237821918091848NN
inevitable0.064698038783335JJ

Label: 1, with a score of: 0.303827800882822
WordScorePoS
urban0JJ
system0.0519476926891779NN
economic0.0959899298575892JJ
social0.112994637345193JJ
environmental0.0442083771593746JJ
objective0NN
raise0NN
challenge0NN
policy0.0450489732120807NN
trade0NN
inevitable0JJ

For instance an ambitious economic strategy in city may be hindered if the city cannot provide low income housing and adequate transportation for workers who will be attracted by jobs

Label: 11, with a score of: 0.91654567335721
WordScorePoS
economic0.0389167984372011JJ
strategy0NN
city0.607697021770652NN
provide0.0269743071558856VB
income0NN
housing0.219415728260093NN
adequate0.0631828116343571JJ
transportation0.04477822029736NN
worker0NN
attract0VB
job0.0136980135278128NN

Label: 8, with a score of: 0.23460655971231
WordScorePoS
economic0.128463821595706JJ
strategy0.0295821503613099NN
city0NN
provide0VB
income0NN
housing0NN
adequate0JJ
transportation0NN
worker0.121575732851624NN
attract0VB
job0.135650868266471NN

Label: 10, with a score of: 0.15239540852129
WordScorePoS
economic0.103276847978089JJ
strategy0NN
city0NN
provide0VB
income0.166475342664294NN
housing0NN
adequate0JJ
transportation0NN
worker0NN
attract0VB
job0NN

Cross sectoral cooperation is key for integrated PART I

Label: 7, with a score of: 0.531248647850726
WordScorePoS
cooperation0.0229264916384433NN
key0.0625160381065121NN
integrate0VB

Label: 15, with a score of: 0.446783761181104
WordScorePoS
cooperation0NN
key0.0528409850971592NN
integrate0.0190167691930804VB

Label: 11, with a score of: 0.334318952624165
WordScorePoS
cooperation0NN
key0NN
integrate0.0537696560159885VB

Label: 6, with a score of: 0.315320587144714
WordScorePoS
cooperation0.0255958462813622NN
key0NN
integrate0.0251182378961834VB

WHY URBAN SUSTAINABILITY MATTERS economic development in cities

Label: 11, with a score of: 0.949217265015231
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
economic0.0389167984372011JJ
development0.0100241617629471NN
city0.607697021770652NN

Label: 8, with a score of: 0.136327443464078
WordScorePoS
urban0JJ
sustainability0NN
economic0.128463821595706JJ
development0.0055149369084567NN
city0NN

There are also significant opportunities to bring about equitable and inclusive development under the umbrella of green growth

Label: 8, with a score of: 0.57190191570413
WordScorePoS
opportunity0.0361300980868102NN
bring0VB
equitable0JJ
inclusive0.0214106369326177JJ
development0.0055149369084567NN
green0JJ
growth0.208565600657229NN

Label: 11, with a score of: 0.426858962446807
WordScorePoS
opportunity0.0164178645080222NN
bring0.0731385760866977VB
equitable0JJ
inclusive0.0583751976558017JJ
development0.0100241617629471NN
green0.04477822029736JJ
growth0NN

Label: 10, with a score of: 0.370098992521423
WordScorePoS
opportunity0.029046330719385NN
bring0VB
equitable0JJ
inclusive0.0172128079963482JJ
development0.0177346522508396NN
green0JJ
growth0.11178243319132NN

The informal sector provides both an immense labor resource as well as market for green services and products

Label: 9, with a score of: 0.519636118534774
WordScorePoS
sector0.0565629593551219NN
immense0.0602029956716497JJ
resource0.0185001613201543NN
market0.0377086395700813NN
green0JJ
service0.0148023490369527NN
product0.0442597400933733NN

Label: 2, with a score of: 0.357930815645433
WordScorePoS
sector0.0182713919265625NN
immense0JJ
resource0.0358563662904843NN
market0.0913569596328122NN
green0JJ
service0.0143446978608332NN
product0NN

Label: 11, with a score of: 0.321558426136553
WordScorePoS
sector0NN
immense0JJ
resource0.0449504361447405NN
market0NN
green0.04477822029736JJ
service0.0269743071558856NN
product0.0268848280079943NN

Label: 8, with a score of: 0.317383518307766
WordScorePoS
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Therefore the job opportunities created by green industries can and should include the urban poor

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Policies that correct environmental issues may have negative or positive side effects leading to either tradeoffs or synergies

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For instance transportation policy that decreases congestion improves inhabitants well being enhances economic attractiveness reduces inequalities in accessibility among neighborhoods and lowers air pollution

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On the other hand reserving urban land for public parks or green spaces without providing compensatory measures may lead to reduced population density increased greenhouse gas emissions from transport and higher land prices

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WordScorePoS
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These conflicts create implementation problems while synergies offer opportunities for win win solutions

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To identify and capitalize on these opportunities cities and their partners can ' Address Knowledge Gaps

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There are massive gaps in terms of knowledge analytics indicators and local government capacities particularly for dealing with complex issues on multiple timescales see Chapters and

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The lack of institutional capacity will be especially limiting when it comes to choosing among technical packages negotiating with suppliers of so called green technology and ensuring community participation when understanding of the global bads remains minimal

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' Foster Participation

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A city metabolism the flow of materials and energy into and out of city see Chapter results from the interactions of many stakeholders including city officials inhabitants nongovernmental organi zations NGOs and businesses

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Sustainable urban policies will depend on the contributions of both public and private actors and on incentives to guide individual private action including funding innovative new technologies and sharing of information see Chapters and

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' Seek Behavioral Change

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Most importantly sustainable development calls for changes in individual and corporate behavior

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Influencing human behavior is possible for instance through the provision of information on energy cost saving measures

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Cities can set their long term objectives for example reduce percent of energy consumption over years and help private actors plan and contribute to these objectives

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The role of the private sector is particularly important in supplying greener goods and services retrofitting buildings and enabling cities to increase density and improve the efficiency of service delivery see Chapter

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The above interventions require strong institutions and an effective regulatory framework discussed in Chapter

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To motivate these institutions and policies however an economic case needs to be made for sustainable cities

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The next chapter explores how green investments relate to urban prosperity and growth

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Further Reading Annex shows projected growth in urban populations

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Annex reprints the Sustainable Development Goals agreed at the Rio summit

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See the case of Mexico City which has worked with World Resources Institute and WBCSD and has obtained the cooperation of firms who contribute percent of the city greenhouse gas emissions

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New York sustainability program announced in is another good example of effective city strategies for sustainability Newman and Jennings

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Economics of Green Cities Key Messages ' Green policies pay dividends both in the short and long run

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They can not only reduce pollution and waste but raise well being and speed economic growth

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' For example city form profoundly influences greenhouse gas emissions and urban sustainability as well as economic productivity and efficiency

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Dense cities that are served by integrated public transport systems can have high prosperity with relatively low emissions

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' While green investments may be profitable market uncertainty in the short term will require support from the public sector to compensate for lack of information

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' An economic downturn may be an ideal time for public investments in sustainability which can boost productivity and employment

Label: 8, with a score of: 0.584186951629441
WordScorePoS
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Label: 9, with a score of: 0.476874740205236
WordScorePoS
economic0.0320338181740375JJ
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Label: 10, with a score of: 0.319438444430631
WordScorePoS
economic0.103276847978089JJ
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As the world seeks to recover from the financial crisis and the subsequent sovereign debt hangover the focus has inevitably shifted away from designing climate policies and other steps toward sustainable development

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WordScorePoS
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WordScorePoS
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Label: 1, with a score of: 0.335056935088144
WordScorePoS
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But the need for long term policies is as acute as ever especially in cities that are rapidly building up infrastructure

Label: 11, with a score of: 0.818730996034131
WordScorePoS
term0NN
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WordScorePoS
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Label: 9, with a score of: 0.232817142120785
WordScorePoS
term0NN
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In fact investing in sustainable choices for cities can be economically rewarding feasible and prudent even in bad economy

Label: 11, with a score of: 0.933177562462521
WordScorePoS
invest0VB
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Investments and business partnerships at the city level will be crucial ingredient in green growth that reduces poverty while protecting natural resources Box

Label: 11, with a score of: 0.643801628600844
WordScorePoS
investment0NN
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Label: 17, with a score of: 0.431528297653628
WordScorePoS
investment0.0766240489430275NN
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Label: 1, with a score of: 0.403332302820652
WordScorePoS
investment0.0376648791150643NN
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Label: 8, with a score of: 0.282552365645186
WordScorePoS
investment0.0252035516550537NN
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Photo Julianne Baker Gallegos World Bank Protecting the environment contributes to national income in different ways

Label: 10, with a score of: 0.423364465146268
WordScorePoS
world0.00276879546803864NN
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environment0NN
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Label: 2, with a score of: 0.554528123527943
WordScorePoS
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Label: 12, with a score of: 0.378982157153751
WordScorePoS
world0.00464349782489754NN
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Label: 9, with a score of: 0.331541903931021
WordScorePoS
world0.00257642711761688NN
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environment0.0221298700466866NN
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First natural capital is part of production

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WordScorePoS
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Environmental conservation increases natural capital and hence income

Label: 10, with a score of: 0.519713102718652
WordScorePoS
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Label: 9, with a score of: 0.400280516072502
WordScorePoS
environmental0.0221298700466866JJ
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Label: 15, with a score of: 0.377548801614907
WordScorePoS
environmental0JJ
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Label: 1, with a score of: 0.339310966698577
WordScorePoS
environmental0.0442083771593746JJ
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Label: 12, with a score of: 0.323981698430615
WordScorePoS
environmental0.0598270388037045JJ
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Second environmental assets are generally prone to market failures externalities and ill defined property rights are common and correcting these market failures can increase the effective supply of natural capital

Label: 12, with a score of: 0.385214943210768
WordScorePoS
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Label: 1, with a score of: 0.355698386942204
WordScorePoS
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It can also improve human well being directly or indirectly

Label: 6, with a score of: 0.534575111895871
WordScorePoS
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Label: 11, with a score of: 0.457737839734643
WordScorePoS
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Label: 4, with a score of: 0.382583422960318
WordScorePoS
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For example alleviating traffic congestion directly reduces air pollution but also indirectly improves the productivity and economies of scale typically offered by cities

Label: 11, with a score of: 0.896997210336853
WordScorePoS
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The UNEP report Towards Green Economy UNEP shares some encouraging news

Label: 8, with a score of: 0.575415018196534
WordScorePoS
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Label: 12, with a score of: 0.393731643863101
WordScorePoS
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Label: 13, with a score of: 0.382342399141602
WordScorePoS
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Label: 7, with a score of: 0.328007714919041
WordScorePoS
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Label: 17, with a score of: 0.317948274654061
WordScorePoS
report0NN
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First investing two percent of global GDP into key sectors could kick start transition to low carbon resource efficient Green Economy

Label: 7, with a score of: 0.428154528282639
WordScorePoS
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Label: 13, with a score of: 0.424956449452761
WordScorePoS
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Label: 14, with a score of: 0.368304158343581
WordScorePoS
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Label: 2, with a score of: 0.310493995047601
WordScorePoS
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Second the shift of resources would not only preserve economic growth but could enable higher growth rate as it would promote new activities and increased job creation

Label: 8, with a score of: 0.803812388702066
WordScorePoS
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Label: 1, with a score of: 0.322423443945479
WordScorePoS
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Label: 10, with a score of: 0.311243666439681
WordScorePoS
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Third the problem at stake involves more than trade offs between growth and environment it is mostly gross misallocation of capital

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WordScorePoS
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Label: 10, with a score of: 0.391299578234672
WordScorePoS
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Label: 12, with a score of: 0.343125033723858
WordScorePoS
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trillion less than percent of the world annual investments were redirected to green investments growth and poverty reduction would be achievable while simultaneously promoting more sustainable economy

Label: 8, with a score of: 0.573411854869893
WordScorePoS
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Label: 1, with a score of: 0.526979776098608
WordScorePoS
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Label: 10, with a score of: 0.345595912994952
WordScorePoS
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Label: 17, with a score of: 0.304717768226737
WordScorePoS
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b Such green economy is not only relevant in developed economies but is also catalyst for growth and poverty reduction in developing countriesc

Label: 8, with a score of: 0.652715999438235
WordScorePoS
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Label: 1, with a score of: 0.471943949047417
WordScorePoS
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Label: 13, with a score of: 0.354575616340467
WordScorePoS
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Label: 10, with a score of: 0.318775734826132
WordScorePoS
green0JJ
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Green growth encompasses not only traditional industries that are becoming less resource intensive but also entirely new industries that provide services Photo Shutterstock Several multilateral institutions have launched initiatives to address the challenges of climate change while providing for the needs of some billion poor people UNEP OECD World Bank

Label: 13, with a score of: 0.527733421346995
WordScorePoS
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Label: 9, with a score of: 0.315748694454474
WordScorePoS
green0JJ
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Label: 1, with a score of: 0.314614388491762
WordScorePoS
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Label: 8, with a score of: 0.281598091202995
WordScorePoS
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Label: 16, with a score of: 0.20695156228446
WordScorePoS
green0JJ
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Label: 2, with a score of: 0.313518968261277
WordScorePoS
green0JJ
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Green growth is about making growth processes resource efficient cleaner and more resilient without necessarily slowing them Hallegate et al

Label: 8, with a score of: 0.716860987124152
WordScorePoS
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Label: 9, with a score of: 0.345739300370375
WordScorePoS
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BOX The Push for Green Growth such as reducing pollution or producing green power

Label: 8, with a score of: 0.545658233956495
WordScorePoS
green0JJ
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Label: 12, with a score of: 0.370565837929108
WordScorePoS
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Label: 10, with a score of: 0.364021219611893
WordScorePoS
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Label: 11, with a score of: 0.359887234989951
WordScorePoS
green0.04477822029736JJ
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Label: 14, with a score of: 0.35102943357274
WordScorePoS
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These create new products new jobs and new collaborative strategies OECD

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WordScorePoS
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Label: 9, with a score of: 0.446071865336785
WordScorePoS
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Label: 12, with a score of: 0.411937642896178
WordScorePoS
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Emerging green industries present opportunities for countries such as China and India which are now industry leaders in wind and solar power and Brazil the world leader in biofuels

Label: 9, with a score of: 0.933931584403386
WordScorePoS
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Morocco and other North African countries are investing heavily in concentrated solar energy with the hope of developing domestic industry

Label: 7, with a score of: 0.81182183512446
WordScorePoS
north0RB
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Label: 9, with a score of: 0.472635577307411
WordScorePoS
north0RB
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Label: 12, with a score of: 0.219871980357444
WordScorePoS
north0RB
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At the city level the rapid expansion of new towns brings enormous opportunities for planning and developing denser and more efficient cities improving urban transport and preventing slum formation

Label: 11, with a score of: 0.985514054025804
WordScorePoS
city0.607697021770652NN
level0.00692515656684925NN
rapid0.0373517915079663JJ
expansion0.0731385760866977NN
bring0.0731385760866977VB
opportunity0.0164178645080222NN
plan0.0583751976558017NN
develop0VB
improve0.0273960270556257VB
urban0.27622591898666JJ
transport0.0747035830159326NN
prevent0VB
slum0.146277152173395NN

This kind of growth is compatible with the idea of green economy that results in improved human well being and social equity while significantly reducing environmental risks and ecological scarcities

Label: 6, with a score of: 0.491449316287324
WordScorePoS
kind0NN
growth0NN
idea0NN
green0JJ
economy0NN
result0.0683326727248364NN
improve0.0511916925627245VB
human0.0127979231406811JJ
social0JJ
reduce0.00936547854413134VB
environmental0JJ
risk0NN
scarcity0.273330690899345NN

Label: 8, with a score of: 0.481258974793854
WordScorePoS
kind0NN
growth0.208565600657229NN
idea0NN
green0JJ
economy0.0821985033584295NN
result0NN
improve0.0150723186962745VB
human0.0150723186962745JJ
social0.0504071033101074JJ
reduce0.0055149369084567VB
environmental0.0295821503613099JJ
risk0NN
scarcity0NN

Label: 11, with a score of: 0.420994166864679
WordScorePoS
kind0NN
growth0NN
idea0.0731385760866977NN
green0.04477822029736JJ
economy0NN
result0NN
improve0.0273960270556257VB
human0.0273960270556257JJ
social0.0458109463416786JJ
reduce0.0200483235258943VB
environmental0.0537696560159885JJ
risk0.0631828116343571NN
scarcity0NN

Label: 10, with a score of: 0.331598471165199
WordScorePoS
kind0NN
growth0.11178243319132NN
idea0NN
green0JJ
economy0NN
result0NN
improve0.0121171980355011VB
human0JJ
social0.101310366625194JJ
reduce0.0310356414389692VB
environmental0.0237821918091848JJ
risk0NN
scarcity0NN

Label: 1, with a score of: 0.328701908727036
WordScorePoS
kind0NN
growth0.0519476926891779NN
idea0NN
green0JJ
economy0NN
result0NN
improve0VB
human0JJ
social0.112994637345193JJ
reduce0.0164833460638534VB
environmental0.0442083771593746JJ
risk0.0519476926891779NN
scarcity0NN

The sectors include agriculture buildings energy supply fisheries forestry industry including energy efficiency tourism transport waste management and water

Label: 7, with a score of: 0.640317296913847
WordScorePoS
sector0NN
include0VB
agriculture0NN
building0NN
energy0.494617163780726NN
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forestry0NN
industry0NN
tourism0NN
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waste0.0625160381065121NN
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water0NN

Label: 12, with a score of: 0.465619828283859
WordScorePoS
sector0.0169905820958638NN
include0VB
agriculture0.0277064208763707NN
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energy0.133960988253756NN
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forestry0NN
industry0NN
tourism0.0332151194779746NN
transport0.0277064208763707NN
waste0.138532104381853NN
management0.0339811641917276NN
water0.186896403054502NN

Label: 6, with a score of: 0.431979523997356
WordScorePoS
sector0.0214003646985842NN
include0VB
agriculture0NN
building0.0348974218717993NN
energy0.0153390539205676NN
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forestry0NN
industry0NN
tourism0NN
transport0NN
waste0NN
management0.0428007293971684NN
water0.556409482163189NN

Label: 9, with a score of: 0.240578687580684
WordScorePoS
sector0.0565629593551219NN
include0VB
agriculture0NN
building0NN
energy0.0540565419125769NN
supply0NN
forestry0NN
industry0.181897200573757NN
tourism0NN
transport0.030745604615229NN
waste0NN
management0NN
water0.0377086395700813NN

Label: 2, with a score of: 0.163220876800801
WordScorePoS
sector0.0182713919265625NN
include0VB
agriculture0.148975141598503NN
building0NN
energy0.0130963126055467NN
supply0NN
forestry0.0583416713841148NN
industry0NN
tourism0NN
transport0NN
waste0.0297950283197006NN
management0NN
water0NN

Label: 17, with a score of: 0.140786273459894
WordScorePoS
sector0.0459744293658165NN
include0VB
agriculture0NN
building0.149940347231585NN
energy0.0109843026967392NN
supply0NN
forestry0NN
industry0NN
tourism0NN
transport0.0249900578719309NN
waste0NN
management0NN
water0NN

billion is roughly equal to the amount of subsidies spent in fossil fuels UNEP

Label: 14, with a score of: 0.450657454923651
WordScorePoS
equal0JJ
amount0NN
subsidy0.162850158165781NN
fossil0NN
fuel0NN

Label: 5, with a score of: 0.470527225846457
WordScorePoS
equal0.114211783066808JJ
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subsidy0NN
fossil0NN
fuel0.0558185411034276NN

Label: 7, with a score of: 0.463276228376951
WordScorePoS
equal0JJ
amount0NN
subsidy0NN
fossil0.0924643738905717NN
fuel0.0749457204978913NN

Label: 12, with a score of: 0.389152234498245
WordScorePoS
equal0JJ
amount0NN
subsidy0.0664302389559493NN
fossil0.0409791940864438NN
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An investment of

Label: 17, with a score of: 0.712629944062538
WordScorePoS
investment0.0766240489430275NN

Label: 7, with a score of: 0.356548199825461
WordScorePoS
investment0.0383371018038178NN

Label: 9, with a score of: 0.350703285433035
WordScorePoS
investment0.0377086395700813NN

Label: 1, with a score of: 0.350296298187632
WordScorePoS
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percent of global GDP each year in energy efficiency and renewable energy would cut primary energy demand by percent in and percent in

Label: 7, with a score of: 0.938518274659014
WordScorePoS
global0.0251662980592747JJ
gdp0NN
energy0.494617163780726NN
renewable0.158624482267508JJ
cut0NN
primary0JJ
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Label: 12, with a score of: 0.251914161087291
WordScorePoS
global0.022306853314744JJ
gdp0NN
energy0.133960988253756NN
renewable0.0234335422829798JJ
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Savings on capital and fuel costs would average million per year between and

Label: 10, with a score of: 0.678752802144057
WordScorePoS
fuel0NN
cost0.0660824365173543NN
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Label: 13, with a score of: 0.61795903375256
WordScorePoS
fuel0NN
cost0.0279982034978914NN
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Urban Density Efficiency and Productivity To address complex environmental problems while sustaining growing consumption cities need to consider their urban design and the policies that affect spatial form and density

Label: 11, with a score of: 0.879230565045824
WordScorePoS
building0.0747035830159326NN
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environmental0.0537696560159885JJ
sustain0VB
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city0.607697021770652NN
policy0.0136980135278128NN
form0NN

Label: 12, with a score of: 0.287722812740079
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
urban0JJ
density0NN
productivity0NN
environmental0.0598270388037045JJ
sustain0.0332151194779746VB
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consumption0.332151194779746NN
city0NN
policy0.0203215381458133NN
form0NN

Label: 8, with a score of: 0.204849433998312
WordScorePoS
building0NN
sustainability0NN
world0.00172202218602071NN
urban0JJ
density0NN
productivity0.0410992516792148NN
environmental0.0295821503613099JJ
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consumption0.147812295323721NN
city0NN
policy0.0452169560888236NN
form0.0591643007226199NN

Label: 16, with a score of: 0.15778538993032
WordScorePoS
building0.109790895760528NN
sustainability0NN
world0NN
urban0JJ
density0NN
productivity0NN
environmental0JJ
sustain0VB
grow0VB
consumption0NN
city0NN
policy0.0201317944152378NN
form0.158049144643351NN

Label: 17, with a score of: 0.139852257438382
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
urban0JJ
density0NN
productivity0NN
environmental0JJ
sustain0VB
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consumption0NN
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policy0.0641521854453136NN
form0NN

Density has been found to affect both productivity and efficiency Some studies suggest that controlling for other factors doubling of density can add from percent to percent productivity Avent

Label: 9, with a score of: 0.608190206420121
WordScorePoS
density0NN
productivity0.0922368138456871NN
study0NN
suggest0.0602029956716497VB
control0NN
double0RB
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In contrast others argue that families flee city centers because of opportunity costs from high density but this may depend on the city history of spatial development Box In addition dense cities tend to have lower per capita emissions provided that they are also served by good public transport systems Hoornweg Sugar and Gomez

Label: 11, with a score of: 0.959330362771751
WordScorePoS
family0NN
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opportunity0.0164178645080222NN
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capita0.04477822029736NN
emission0.0373517915079663NN
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Label: 13, with a score of: 0.117357045578619
WordScorePoS
family0NN
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emission0.19598742448524NN
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Cities with limited urban sprawl and integrated urban transit systems such as Barcelona and Singapore have become affluent while keeping their per capita emissions low see Chapter for comparison of emissions and GDP among large cities

Label: 11, with a score of: 0.964486785430972
WordScorePoS
city0.607697021770652NN
limited0JJ
urban0.27622591898666JJ
integrate0.0537696560159885VB
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emission0.0373517915079663NN
gdp0NN

Label: 13, with a score of: 0.20149719572588
WordScorePoS
city0NN
limited0JJ
urban0JJ
integrate0.0201523636533764VB
system0NN
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emission0.19598742448524NN
gdp0NN

Their relatively low resource intensity is mainly result of greater transport energy efficiency due BOX to reduced distances and greater shares of public transport modes

Label: 7, with a score of: 0.734629286834877
WordScorePoS
resource0NN
intensity0.122412709674631NN
result0NN
transport0NN
energy0.494617163780726NN
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Label: 12, with a score of: 0.338297430743371
WordScorePoS
resource0.0416785799329393NN
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Label: 11, with a score of: 0.356420471698712
WordScorePoS
resource0.0449504361447405NN
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transport0.0747035830159326NN
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due0JJ
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Burdett reports that compact cities such as Vienna or Barcelona have significantly higher population densities higher public transport use and correspondingly lower per capita emissions than sprawling cities such as Atlanta and Houston

Label: 11, with a score of: 0.971781710195805
WordScorePoS
report0NN
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population0.0194583992186006NN
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emission0.0373517915079663NN

Label: 13, with a score of: 0.125037433124174
WordScorePoS
report0NN
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population0NN
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emission0.19598742448524NN

Higher density also enables more energy efficient heating and cooling in buildings and lower embedded energy demand for urban infrastructure

Label: 7, with a score of: 0.829909693825125
WordScorePoS
density0NN
enable0VB
energy0.494617163780726NN
heating0.122412709674631NN
building0NN
demand0NN
urban0JJ
infrastructure0.0651354046449052NN

Label: 11, with a score of: 0.408603654481993
WordScorePoS
density0.0731385760866977NN
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energy0.0492535935240667NN
heating0NN
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urban0.27622591898666JJ
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Label: 9, with a score of: 0.233476655690529
WordScorePoS
density0NN
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energy0.0540565419125769NN
heating0NN
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demand0.030745604615229NN
urban0JJ
infrastructure0.192202909044225NN

Label: 12, with a score of: 0.209306567601146
WordScorePoS
density0NN
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energy0.133960988253756NN
heating0NN
building0NN
demand0NN
urban0JJ
infrastructure0.028867295332595NN

Label: 17, with a score of: 0.165665386087315
WordScorePoS
density0NN
enable0.0499801157438617VB
energy0.0109843026967392NN
heating0NN
building0.149940347231585NN
demand0NN
urban0JJ
infrastructure0.0130185595639838NN

The savings in operating costs from shorter transport networks and less diffuse utility infrastructure can amount to thousands of dollars of annual savings for the average household Litman

Label: 10, with a score of: 0.500396125747897
WordScorePoS
operate0VB
cost0.0660824365173543NN
transport0NN
infrastructure0NN
amount0NN
dollar0NN
average0.146608885563018NN
household0.0330412182586772NN

Label: 9, with a score of: 0.453999996591881
WordScorePoS
operate0VB
cost0NN
transport0.030745604615229NN
infrastructure0.192202909044225NN
amount0NN
dollar0NN
average0NN
household0NN

Label: 13, with a score of: 0.451334247526101
WordScorePoS
operate0VB
cost0.0279982034978914NN
transport0NN
infrastructure0NN
amount0.0279982034978914NN
dollar0NN
average0.1656430212089NN
household0NN

Label: 12, with a score of: 0.394938739168332
WordScorePoS
operate0.0542519672965169VB
cost0.0277064208763707NN
transport0.0277064208763707NN
infrastructure0.028867295332595NN
amount0NN
dollar0NN
average0NN
household0.0554128417527413NN

And compact well managed cities with intelligent infrastructure can be more attractive to walkers than suburban or rural communities

Label: 11, with a score of: 0.868115415785568
WordScorePoS
manage0VB
city0.607697021770652NN
infrastructure0.0194583992186006NN
rural0.0373517915079663JJ
community0NN

Label: 2, with a score of: 0.335264779665636
WordScorePoS
manage0.0297950283197006VB
city0.0440683498519077NN
infrastructure0.0155217067874935NN
rural0.148975141598503JJ
community0.0182713919265625NN

Label: 9, with a score of: 0.275726178006585
WordScorePoS
manage0VB
city0NN
infrastructure0.192202909044225NN
rural0JJ
community0.0188543197850406NN

Inner city Barcelona London Paris and Rome together with New York Singapore and Tokyo provide examples of creative growing city centers with access to variety of amenities including green space

Label: 11, with a score of: 0.980758859611103
WordScorePoS
city0.607697021770652NN
paris0NN
provide0.0269743071558856VB
grow0.0229054731708393VB
access0.00626002771693937NN
variety0NN
include0VB
green0.04477822029736JJ
space0.04477822029736NN

Historically urban density or sprawl has mostly not been determined by policy

Label: 11, with a score of: 0.900216107645154
WordScorePoS
urban0.27622591898666JJ
density0.0731385760866977NN
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policy0.0136980135278128NN

Label: 10, with a score of: 0.331485355484725
WordScorePoS
urban0.0488696285210061JJ
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Some cities are based on medieval or ancient road plans

Label: 11, with a score of: 0.981147428102669
WordScorePoS
city0.607697021770652NN
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Others have been developed for car travel on the basis Do Families Prefer the Suburbs

Label: 12, with a score of: 0.72599116525016
WordScorePoS
develop0VB
car0.0542519672965169NN
travel0.0542519672965169NN
basis0NN
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Some economists argue that low urban density is preferable based on hedonic estimation that is people subjective preferences

Label: 11, with a score of: 0.935355500316122
WordScorePoS
urban0.27622591898666JJ
density0.0731385760866977NN
base0NN
people0.0161552501392041NNS

Families may accept lower wages and higher commuting costs in order to live away from city centers and afford larger living quarters for example

Label: 11, with a score of: 0.918306976647082
WordScorePoS
family0NN
cost0NN
live0.0269743071558856VB
city0.607697021770652NN
quarter0NN

As with all issues of path dependency people preferences will depend on how spatial development unfolds over time

Label: 15, with a score of: 0.425408048460852
WordScorePoS
issue0NN
people0.0159982027144574NNS
depend0.0633470357346975VB
development0.0106357666046841NN
time0.016202004209163NN

In cities such as Cleveland Pittsburgh Buffalo or Detroit in the United States as in several Latin American capitals suburban sprawl has drawn wealthier car owning families away from inner cities that can then become run down poor and crimeridden the so called hollowing out effect

Label: 11, with a score of: 0.965352202382128
WordScorePoS
city0.607697021770652NN
unite0VB
wealthier0JJR
car0NN
family0NN
poor0.0229054731708393JJ
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Label: 1, with a score of: 0.117450983434846
WordScorePoS
city0NN
unite0VB
wealthier0JJR
car0NN
family0NN
poor0.150659516460257JJ
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In other cities however wealthy people congregate in the city centers and tolerate high housing costs precisely because of superior living environment

Label: 11, with a score of: 0.982910866431099
WordScorePoS
city0.607697021770652NN
people0.0161552501392041NNS
housing0.219415728260093NN
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live0.0269743071558856VB
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This supports high quality housing amenities and wealth of cultural opportunities

Label: 11, with a score of: 0.82609119026586
WordScorePoS
support0.0224752180723703NN
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housing0.219415728260093NN
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opportunity0.0164178645080222NN

Label: 4, with a score of: 0.348086503902636
WordScorePoS
support0NN
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Examples exist across the world from Paris to Hong Kong Tokyo to New York

Label: 0, with a score of: 1

Nonetheless suburban living remains popular and cities need to be carefully planned in order to attract wealth creating individuals

Label: 11, with a score of: 0.942053888875195
WordScorePoS
live0.0269743071558856VB
remains0NNS
city0.607697021770652NN
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WHY URBAN SUSTAINABILITY MATTERS of land intensive suburbanization

Label: 11, with a score of: 0.864126635306031
WordScorePoS
urban0.27622591898666JJ
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land0.0389167984372011NN
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Label: 15, with a score of: 0.339663756233761
WordScorePoS
urban0JJ
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land0.123873694868802NN
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Highly dense cities such as Barcelona and Manhattan have had their scope for sprawl limited by the constraints of oceans and mountains as well as strong public policy and local interest in compactness

Label: 11, with a score of: 0.748559070719753
WordScorePoS
highly0RB
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limited0JJ
constraint0NN
ocean0NN
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strong0JJ
public0.0583751976558017NN
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local0.0194583992186006JJ

Label: 14, with a score of: 0.523017611640098
WordScorePoS
highly0RB
city0NN
limited0JJ
constraint0NN
ocean0.488550474497343NN
mountain0NN
strong0JJ
public0NN
policy0NN
local0JJ

Whatever the reason once an urban form is chosen and locked in it will determine the pattern of city resource intensity for decades or even centuries

Label: 11, with a score of: 0.863182400814309
WordScorePoS
urban0.27622591898666JJ
form0NN
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decade0.055245183797332NN
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Label: 13, with a score of: 0.337632282724529
WordScorePoS
urban0JJ
form0NN
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pattern0.0414107553022249NN
city0NN
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decade0.124232265906675NN
century0.219293228426234NN

Label: 16, with a score of: 0.138626833765784
WordScorePoS
urban0JJ
form0.158049144643351NN
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pattern0NN
city0NN
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intensity0NN
decade0NN
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When densities are too low bike lanes or bus systems for example become too expensive and unappealing

Label: 11, with a score of: 0.686104050504527
WordScorePoS
density0.0731385760866977NN
system0.0315914058171786NN
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Label: 2, with a score of: 0.495269436010414
WordScorePoS
density0NN
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It is probably too late to make Phoenix and Atlanta efficient dense cities but measures such as road pricing bus lanes on highways electric vehicle infrastructure and distributed low carbon energy networks can reduce carbon footprints improve energy efficiency and promote innovation in resource intense sprawling cities

Label: 11, with a score of: 0.718386751156262
WordScorePoS
city0.607697021770652NN
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carbon0.0373517915079663NN
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innovation0.0373517915079663NN
resource0.0449504361447405NN

Label: 7, with a score of: 0.553390694073252
WordScorePoS
city0NN
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WordScorePoS
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Some of these cities such as Los Angeles have been able to promote denser forms within the urban core

Label: 11, with a score of: 0.955629376402391
WordScorePoS
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Label: 16, with a score of: 0.194761831264952
WordScorePoS
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Exogenous variables such as the increase in fuel prices financial crises and changing cultural and generational tastes will strongly influence how future cities organize

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Label: 13, with a score of: 0.456590053830784
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Most likely many of those factors will reinforce each other

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Label: 17, with a score of: 0.414319264629018
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For example the New Urbanism movement of the has insisted for decades on the return to mixed use residential areas and compact cities Congress for the New Urbanism

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These ideas have found fertile ground amidst the planning profession as the hike in fuel prices has made commuting an often unaffordable proposition and car dependent suburban houses much less attractive

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This was the case in Victorville miles northeast of downtown Los Angeles where inhabitants were entirely dependent on private cars to connect homes to work and services Karlenzig

Label: 0, with a score of: 1

With the rise in fuel prices some of Victorville suburban neighborhoods have been demolished

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Label: 7, with a score of: 0.411125452947549
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Many scholars predict the end of sprawl and the emergence of decentralized urban form based on the replacement of the private car by efficient public transportation and light rail as in European countries

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Lessons from our collective experience with urbanization should be used to support developing cities that are expected to grow exponentially

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Co benefits of Reducing Greenhouse Gas Emissions Implementing sustainability strategies often pays short term economic dividends

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Greenhouse gas reduction plans can drive efficiency and allow cities to reduce waste and cut costs

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Cities offer unique environment to innovate develop and scale up new ideas and processes promoting the growth of knowledge intensive green production sectors

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Urban economies of scale offer the opportunity to develop green investments such as integrated public transit sewers and water systems congestion pricing smart grids smart buildings and decentralized energy networks Sedgley Norman and Elmslie

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WordScorePoS
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Label: 7, with a score of: 0.483280693104419
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Label: 6, with a score of: 0.480810511554637
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Label: 12, with a score of: 0.299349406533588
WordScorePoS
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Label: 17, with a score of: 0.221227060078632
WordScorePoS
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Especially in OECD countries some cities have become laboratories for action on climate change in which growing experience leads to further innovation and lowers the cost of new technologies

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Label: 11, with a score of: 0.576537676983773
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Urban regions already produce times more renewable technology patents than rural regions KamalChaoui and Roberts p

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Label: 11, with a score of: 0.416611804027488
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Label: 2, with a score of: 0.366224477436335
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Label: 7, with a score of: 0.356774997994241
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Label: 3, with a score of: 0.348664657653032
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Climate policy also yields other collateral benefits at the local level and conversely investment in attractive and successful cities will yield climate benefits

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Label: 11, with a score of: 0.590682473088605
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Low particulate pollution reduces health care costs increases city attractiveness and promotes competitiveness

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Similarly reduced waste makes for more attractive environment with fewer and smaller landfills for example while renewable energy sources enhance energy security Hallegatte et al

Label: 7, with a score of: 0.886766349873602
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Label: 12, with a score of: 0.355332496809866
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Policies to increase vegetation and green spaces not only reduce the heat island effect but also improve resilience to flooding

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Label: 17, with a score of: 0.330939170719408
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Low carbon transportation means fewer traffic jams and accidents as well as cleaner air BOX and healthier people Box

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carbon0.0625160381065121NN
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Label: 11, with a score of: 0.412402176212459
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Efficient and green cities are likely to draw communities together as they provide better places to live and generate economic prosperity

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Because of these synergies between sustainability and livability the returns to complementary integrated policies are multiplied the sum is greater than the parts

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Incentives Business Opportunities and Challenges Photo Julianne Baker Gallegos World Bank Bogotá investment in the Transmilenio Bus Rapid Transit system has brought important benefits including reduction in travel times diminished congestion reduced carbon dioxide emissions and increased mobility and access to labor markets Montezuma

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Further the scheme was designed to connect the major slum areas around the capital city

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Health benefits from green transport strategies are also significant as they include emission reductions increased physical activity levels and road safety

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Health and safety benefits have been estimated to exceed the cost for integrated non motorized and public transport measures by factor of to times in cities as diverse as Bogotá Delhi and Morogoro Dora

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Photo Shutterstock Case Study Benefits of Bus Rapid Transit in Bogotá Opportunities in low carbon investment have been estimated at billion per year and rising with clean energy investments in totaling billion UNEP and New Energy Finance see also Box

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Label: 12, with a score of: 0.220599647281284
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total0.0468670845659596NN
finance0NN

Once new markets are created with supportive policies and favorable legal and regulatory environment innovative businesses can explore this growing field

Label: 9, with a score of: 0.491164811230432
WordScorePoS
market0.0377086395700813NN
create0.0520080474473126VB
policy0.011275328195446NN
legal0JJ
environment0.0221298700466866NN
business0.0909486002868787NN
grow0.0188543197850406VB

Label: 12, with a score of: 0.454369541709044
WordScorePoS
market0.0169905820958638NN
create0.0234335422829798VB
policy0.0203215381458133NN
legal0JJ
environment0.079769385071606NN
business0.0409791940864438NN
grow0.0339811641917276VB

Label: 8, with a score of: 0.419853061484557
WordScorePoS
market0.0252035516550537NN
create0.0695218668857431VB
policy0.0452169560888236NN
legal0JJ
environment0.0591643007226199NN
business0NN
grow0VB

Label: 10, with a score of: 0.307038321478744
WordScorePoS
market0.0202620733250388NN
create0VB
policy0.0848203862485078NN
legal0JJ
environment0NN
business0NN
grow0.0405241466500776VB

New activities will include higher end business services such as environmental consulting

Label: 9, with a score of: 0.507796869928832
WordScorePoS
activity0NN
include0VB
business0.0909486002868787NN
service0.0148023490369527NN
environmental0.0221298700466866JJ

Label: 12, with a score of: 0.505959683288166
WordScorePoS
activity0.0199423462679015NN
include0VB
business0.0409791940864438NN
service0.00666957305782837NN
environmental0.0598270388037045JJ

Label: 1, with a score of: 0.351674837158246
WordScorePoS
activity0NN
include0VB
business0NN
service0.0443555132287109NN
environmental0.0442083771593746JJ

Label: 11, with a score of: 0.320622998555632
WordScorePoS
activity0NN
include0VB
business0NN
service0.0269743071558856NN
environmental0.0537696560159885JJ

Label: 8, with a score of: 0.31350486923044
WordScorePoS
activity0.0295821503613099NN
include0VB
business0NN
service0.0197870712294258NN
environmental0.0295821503613099JJ

Clearly opportunities will vary across cities according to income levels human capital and comparative advantages for low carbon transition

Label: 11, with a score of: 0.881927074913828
WordScorePoS
opportunity0.0164178645080222NN
city0.607697021770652NN
income0NN
level0.00692515656684925NN
human0.0273960270556257JJ
carbon0.0373517915079663NN

Label: 10, with a score of: 0.247828206057384
WordScorePoS
opportunity0.029046330719385NN
city0NN
income0.166475342664294NN
level0NN
human0JJ
carbon0NN

WHY URBAN SUSTAINABILITY MATTERS ' The payback from an up front investment in energy efficiency is not immediate sometimes accruing beyond political cycles or over uncertain and long periods that deter private investors

Label: 7, with a score of: 0.673109259157962
WordScorePoS
urban0JJ
sustainability0NN
investment0.0383371018038178NN
energy0.494617163780726NN
political0JJ
cycle0NN
period0NN
private0JJ

Label: 11, with a score of: 0.411073309070713
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
investment0NN
energy0.0492535935240667NN
political0JJ
cycle0NN
period0NN
private0JJ

Label: 12, with a score of: 0.4097393503259
WordScorePoS
urban0JJ
sustainability0.0409791940864438NN
investment0NN
energy0.133960988253756NN
political0JJ
cycle0.108503934593034NN
period0.0409791940864438NN
private0JJ

For efficient Even in the present uncertain environment with lack of ambitious and coordinated global green policies investment in renewable energy generation and energy efficiency is surging

Label: 7, with a score of: 0.842753691965873
WordScorePoS
environment0NN
lack0.0383371018038178NN
coordinate0NN
global0.0251662980592747JJ
green0JJ
policy0NN
investment0.0383371018038178NN
renewable0.158624482267508JJ
energy0.494617163780726NN
generation0NN

Label: 12, with a score of: 0.356689814809993
WordScorePoS
environment0.079769385071606NN
lack0NN
coordinate0NN
global0.022306853314744JJ
green0.0332151194779746JJ
policy0.0203215381458133NN
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renewable0.0234335422829798JJ
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'Investment in this sector has quadrupled since according to Bloomberg New Energy Finance BNEF Zenghelis

Label: 7, with a score of: 0.90324334771823
WordScorePoS
sector0NN
energy0.494617163780726NN
finance0NN

Label: 12, with a score of: 0.275659665151368
WordScorePoS
sector0.0169905820958638NN
energy0.133960988253756NN
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New investment in clean energy surpassed investment in conventional energy generation in rising to between and billion

Label: 7, with a score of: 0.896911630638728
WordScorePoS
investment0.0383371018038178NN
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energy0.494617163780726NN
surpass0VBP
generation0NN
rise0NN

Label: 12, with a score of: 0.263499091784082
WordScorePoS
investment0NN
clean0JJ
energy0.133960988253756NN
surpass0VBP
generation0.0819583881728876NN
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'Two of the world fastest growing economies South Korea and China moved decisively to embrace high technology low carbon growth in their stimulus packages in and and in China outline for its Twelfth Five Year Plan

Label: 8, with a score of: 0.520526660907836
WordScorePoS
world0.00172202218602071NN
grow0VB
economy0.0821985033584295NN
south0RB
move0NN
technology0NN
carbon0NN
growth0.208565600657229NN
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Label: 7, with a score of: 0.422788013584362
WordScorePoS
world0.00261936653839306NN
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economy0.0625160381065121NN
south0RB
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technology0.109914925284606NN
carbon0.0625160381065121NN
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Label: 13, with a score of: 0.411568168263067
WordScorePoS
world0.00351929966767313NN
grow0.0171695137813101VB
economy0.0839946104936743NN
south0RB
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Of the seven magic growth sectors identified in the Twelfth Five Year Plan three are low carbon industries clean energy energy efficiency and clean energy vehicles the other two sectors are in high end manufacturing

Label: 7, with a score of: 0.843548526025603
WordScorePoS
growth0NN
sector0NN
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carbon0.0625160381065121NN
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clean0.125032076213024JJ
energy0.494617163780726NN
vehicle0NN
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Label: 9, with a score of: 0.368846386253044
WordScorePoS
growth0.0260040237236563NN
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carbon0NN
industry0.181897200573757NN
clean0.030745604615229JJ
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manufacture0.240811982686599NN

Label: 12, with a score of: 0.313346006757259
WordScorePoS
growth0NN
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carbon0NN
industry0NN
clean0JJ
energy0.133960988253756NN
vehicle0.217007869186068NN
manufacture0NN

Label: 8, with a score of: 0.121605538867213
WordScorePoS
growth0.208565600657229NN
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carbon0NN
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clean0JJ
energy0NN
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manufacture0NN

' These investments also carry significant policyrelated risk as the financial returns from energy efficiency will depend on energy and emissions policies

Label: 7, with a score of: 0.878186270492007
WordScorePoS
investment0.0383371018038178NN
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energy0.494617163780726NN
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Label: 12, with a score of: 0.276853282849918
WordScorePoS
investment0NN
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return0NN
energy0.133960988253756NN
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emission0.0554128417527413NN
policy0.0203215381458133NN

Label: 13, with a score of: 0.185989959943295
WordScorePoS
investment0NN
risk0NN
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emission0.19598742448524NN
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' The trade offs between more expensive renewable energy and less expensive polluting fuels are difficult to measure or quantify and consumers may be inclined to favor the cheapest solution in the short run

Label: 7, with a score of: 0.697058660795909
WordScorePoS
trade0NN
expensive0JJ
renewable0.158624482267508JJ
energy0.494617163780726NN
pollute0VB
fuel0.0749457204978913NN
measure0NN
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solution0NN

Label: 12, with a score of: 0.649856564584244
WordScorePoS
trade0NN
expensive0.0542519672965169JJ
renewable0.0234335422829798JJ
energy0.133960988253756NN
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fuel0.0332151194779746NN
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' Profitable investments may be precluded by low liquidity and lack of capital to finance upfront investment and compensate for short run losses

Label: 0, with a score of: 1

BOX Clean Energy Investments Surging consumer appliances even where payback periods are short many buyers face financial constraints in making the initial investment

Label: 7, with a score of: 0.761938331280363
WordScorePoS
clean0.125032076213024JJ
energy0.494617163780726NN
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period0NN
constraint0NN

Label: 12, with a score of: 0.547609813648568
WordScorePoS
clean0JJ
energy0.133960988253756NN
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consumer0.325511803779102NN
period0.0409791940864438NN
constraint0NN

Photo Dominic Sansoni World Bank While public finances remain stretched since the economic crisis of there are sufficient private resources that could be invested in green urban technology were it not for the perceived lack of opportunity and confidence Romani Stern and Zenghelis

Label: 11, with a score of: 0.548734567121562
WordScorePoS
world0.00626002771693937NN
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green0.04477822029736JJ
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technology0NN
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Label: 8, with a score of: 0.273008851747639
WordScorePoS
world0.00172202218602071NN
bank0NN
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remain0.049270765107907VB
economic0.128463821595706JJ
private0JJ
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green0JJ
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technology0NN
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Label: 17, with a score of: 0.34241795724358
WordScorePoS
world0.00418824502414673NN
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private0.0898760357768464JJ
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technology0.109843026967392NN
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Label: 5, with a score of: 0.319883245685173
WordScorePoS
world0.00585260443759492NN
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technology0.0409315617631NN
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However although there is evidence that green cities and prosperity go hand in hand private companies acting in their own self interest are not always best placed to benefit from these synergies

Label: 11, with a score of: 0.953629990971842
WordScorePoS
evidence0NN
green0.04477822029736JJ
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prosperity0.0731385760866977NN
private0JJ
company0NN

Label: 10, with a score of: 0.170057373763224
WordScorePoS
evidence0.12939607756667NN
green0JJ
city0NN
prosperity0NN
private0JJ
company0NN

Public incentives remain necessary to encourage greener practices and industries as green growth endeavors may have number of problems attracting investment and producing profits BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Photo Dominic Sansoni World Bank ' Finally the potential gains from investment in energy efficiency and renewables may not yet have been recognized

Label: 7, with a score of: 0.54274073026179
WordScorePoS
public0NN
incentive0NN
remain0VB
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world0.00261936653839306NN
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energy0.494617163780726NN
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Label: 17, with a score of: 0.400553477323485
WordScorePoS
public0.0390556786919513NN
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energy0.0109843026967392NN
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Label: 9, with a score of: 0.383079431246545
WordScorePoS
public0.0160169090870187NN
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recognize0.0368585655748969VB

Label: 12, with a score of: 0.359208629288284
WordScorePoS
public0.028867295332595NN
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recognize0VB

Label: 8, with a score of: 0.281434493267427
WordScorePoS
public0NN
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growth0.208565600657229NN
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world0.00172202218602071NN
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renewable0JJ
recognize0VB

As fossil fuels and other scarce resources continue to rise in price and as the policy environment addresses inefficiencies this should change

Label: 13, with a score of: 0.695331777359883
WordScorePoS
fossil0NN
fuel0NN
scarce0JJ
resource0NN
continue0.0403047273067529VB
rise0.120914181920259NN
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policy0.0102677744436108NN
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address0NN
change0.402565546215053NN

Label: 7, with a score of: 0.330869987170879
WordScorePoS
fossil0.0924643738905717NN
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continue0VB
rise0NN
price0NN
policy0NN
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change0.105749654845006NN

However even where clear gains have existed in the past there have been several additional barriers preventing optimal investment in resource efficiency ' Research in renewable energy is often long term and speculative carrying many risks with knowledge spillovers that are hard to monetize or patent

Label: 7, with a score of: 0.725403041732786
WordScorePoS
gain0NN
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energy0.494617163780726NN
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Label: 17, with a score of: 0.373460848450202
WordScorePoS
gain0NN
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energy0.0109843026967392NN
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knowledge0.0499801157438617NN

Label: 12, with a score of: 0.264451393944825
WordScorePoS
gain0.0664302389559493NN
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Consequently innovation has often fallen short of the social optimum

Label: 9, with a score of: 0.477195106929479
WordScorePoS
innovation0.122982418460916NN
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Label: 8, with a score of: 0.446137914874426
WordScorePoS
innovation0.0821985033584295NN
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Label: 3, with a score of: 0.414138145479686
WordScorePoS
innovation0NN
fall0.123094312756122NN
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Label: 1, with a score of: 0.380158827093826
WordScorePoS
innovation0NN
fall0NN
social0.112994637345193JJ

Label: 10, with a score of: 0.340848300889014
WordScorePoS
innovation0NN
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social0.101310366625194JJ

' Finally lack of expertise often hinders the speed of the change in the urban environment

Label: 13, with a score of: 0.674904089059995
WordScorePoS
lack0NN
expertise0NN
change0.402565546215053NN
urban0JJ
environment0NN

Label: 11, with a score of: 0.599531806246061
WordScorePoS
lack0.0229054731708393NN
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change0.0315914058171786NN
urban0.27622591898666JJ
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' Efficiency gains that boost productivity in the long run often threaten individual jobs triggering political resistance

Label: 8, with a score of: 0.465639612531954
WordScorePoS
gain0NN
productivity0.0410992516792148NN
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individual0JJ
job0.135650868266471NN
political0JJ

Label: 9, with a score of: 0.577729950628776
WordScorePoS
gain0NN
productivity0.0922368138456871NN
threaten0VB
individual0JJ
job0.0902026255635679NN
political0.0368585655748969JJ

Label: 10, with a score of: 0.379148260452196
WordScorePoS
gain0NN
productivity0NN
threaten0VB
individual0.064698038783335JJ
job0NN
political0.0792212041430275JJ

Label: 5, with a score of: 0.339086617258033
WordScorePoS
gain0NN
productivity0NN
threaten0VB
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job0.0170753354211044NN
political0.111637082206855JJ

Despite these barriers cities increasingly lead the field in changing the public perception in favor of sustainable policies often influencing even the national agenda

Label: 11, with a score of: 0.758570699384087
WordScorePoS
barrier0NN
city0.607697021770652NN
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change0.0315914058171786NN
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national0.00501208088147356JJ
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Label: 13, with a score of: 0.444233054385418
WordScorePoS
barrier0NN
city0NN
lead0NN
change0.402565546215053NN
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policy0.0102677744436108NN
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Examples include congestion charging in London car sharing in Berlin and planning and policy leadership in Bandung Barcelona Brisbane Guelph Nanjing and Portland many of which set the standard within their countries

Label: 17, with a score of: 0.689819576180956
WordScorePoS
include0VB
congestion0NN
car0NN
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set0.0978661089756501NN
standard0NN
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As discussed in the previous chapter central and local government agencies are often best positioned to prompt behavioral change by engaging well informed population

Label: 13, with a score of: 0.797441922014663
WordScorePoS
previous0.0548233071065585JJ
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local0.0145856516935579JJ
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change0.402565546215053NN
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Label: 7, with a score of: 0.334899818455423
WordScorePoS
previous0JJ
central0.0924643738905717JJ
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government0NN
agency0NN
change0.105749654845006NN
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' Weak monitoring and measurement systems make it hard to manage and monetize the gains from efficiency investments for example few consumers have smart meters to alert them to energy use and waste which reduces the incentive to invest

Label: 12, with a score of: 0.685489506682995
WordScorePoS
monitor0.0332151194779746NN
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gain0.0664302389559493NN
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waste0.138532104381853NN
reduce0.022306853314744VB
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Label: 7, with a score of: 0.574949606175961
WordScorePoS
monitor0NN
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investment0.0383371018038178NN
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energy0.494617163780726NN
waste0.0625160381065121NN
reduce0.00838876601975825VB
incentive0NN
invest0VB

Thus public intervention with popular and clearly understood mandate is essential for addressing the market failures associated with urban sustainability Rode et al

Label: 11, with a score of: 0.729583544792511
WordScorePoS
public0.0583751976558017NN
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urban0.27622591898666JJ
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Label: 10, with a score of: 0.3293418717386
WordScorePoS
public0.0172128079963482NN
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address0.064698038783335NN
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Label: 2, with a score of: 0.326411516386601
WordScorePoS
public0NN
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mandate0.0583416713841148NN
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Credible policy signals at the city level can leverage private investment in renewable energy smart networks and commu ' There are often split incentives where the benefits of energy savings do not accrue to the individual or group making the investment the landlord construction firm or property seller for example

Label: 7, with a score of: 0.768816326453589
WordScorePoS
credible0JJ
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level0NN
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investment0.0383371018038178NN
renewable0.158624482267508JJ
energy0.494617163780726NN
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Label: 11, with a score of: 0.456334577582266
WordScorePoS
credible0JJ
policy0.0136980135278128NN
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Label: 12, with a score of: 0.19890988394068
WordScorePoS
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WHY URBAN SUSTAINABILITY MATTERS nities energy efficiency and low carbon vehicles while stimulating the local economy

Label: 7, with a score of: 0.705057111448316
WordScorePoS
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Label: 12, with a score of: 0.462394349069255
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Label: 11, with a score of: 0.434981690453843
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One good opportunity is targeted public procurement which affords cities chance to shape markets and incentivize innovation on low carbon products and services Stern

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With output remaining below capacity and the cost of capital at historically low levels there is less fear of crowding out alternative investment or displacing jobs

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Label: 16, with a score of: 0.315677356483895
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While the private sector may remain cautious some percent of cities report that climate change represents an economic opportunity for their city the most commonly reported opportunity is green jobs reported by cities closely followed by development of new business and industries reported by cities CDP

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Label: 13, with a score of: 0.249256802720546
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Label: 9, with a score of: 0.187808100142725
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Recession Investing and Sustainable Finance Among the other co benefits of sustainable cities planning policy can also influence the macroeconomic environment

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During economic downturns urban infrastructure and retrofitting can boost job creation and stimulate activity especially in shovelready sectors such as building efficiency retrofits broadband infrastructure and retooling manufac turers

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Label: 8, with a score of: 0.549124330699982
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Label: 11, with a score of: 0.433612752457577
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Label: 17, with a score of: 0.217513290367094
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In the present environment there is an opportunity to take advantage of the record pool of private savings provided the investment packages have adequate returns for private investment funds Zenghelis

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Public funds could be used to leverage guarantee or otherwise improve the attractiveness of clean and green investments to the private sector

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Label: 17, with a score of: 0.523731662194366
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Label: 9, with a score of: 0.345246569051584
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But this will require creativity in planning and designing new financial instruments to encourage sustainable urbanization

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Label: 8, with a score of: 0.677415613303297
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Indeed now may be an ideal time to invest in sound long term growth strategy and to address the basic market failures hindering green urban investment

Label: 11, with a score of: 0.485544971775784
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Label: 8, with a score of: 0.396706333349385
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Label: 1, with a score of: 0.319655386001364
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It is myth that recessions are bad time to plan green investments because they add to business costs

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Label: 12, with a score of: 0.37632502113815
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For city to be sustainable in the long run it must diversify its capital base and generate cash flow for reinvestment

Label: 11, with a score of: 0.967890335700052
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Although multiple sources of capital are available to city governments and businesses including public private and developmental capital urban sustainability initiatives often fail to secure the investment they require

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WordScorePoS
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Label: 17, with a score of: 0.363402200820318
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To access new sources of finance cities need to create conducive policy and investment environments and articulate the value of their sustainability initiatives in Public Sector Value ofe Public Sector Value Shareholder Value Shareholder Value Consumer Value Consumer Value ces offe Source Adapted from Arup et al

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FIG

Label: 0, with a score of: 1

Value of Urban Sustainability Initiatives for Different Stakeholders BUILDING SUSTAINABILITY IN AN URBANIZING WORLD terms that interest each investor

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For example the private sector will care about revenue growth and productivity the public sector about cost to serve and sustainability targets and citizens about bill savings and well being Figure

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Label: 9, with a score of: 0.458136008725953
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Label: 5, with a score of: 0.410183909138201
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Social Impact Bonds in the UK are an example of how to foster common perception of value

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Label: 10, with a score of: 0.387631246377421
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The UK government created an outcome driven system for solving societal issues that aligns public sector funding with private sector incentives so that there is mutual benefit from the improved outcomes

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The Need for Knowledge To take full advantage of the economic benefits of green policies cities will ultimately need better developed research base

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As we have seen there is mounting evidence that measures that make cities work better in terms of emissions and sustainability also make them more prosperous and attractive

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These data need to be collated in order to develop fuller understanding of the policy mixes that can lead to successful resourceefficient cities

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However problems of data compat http ukpolicymatters

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thelancet

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p ibility and reliability and ultimately the fact that no two cities are alike make the analytical task challenge

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These issues are discussed in Chapter

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First though we will explore the types of interventions that need to be studied the best opportunities for making cities cleaner more efficient and more prepared for climate change and other shocks

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There is no one size fits all solution for complex and heterogeneous cities but all have scope to increase efficiency make greater use of renewable resources and improve the environment for innovation with significant economic as well as environmental returns

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environment0NN
innovation0NN
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Label: 9, with a score of: 0.31913643880492
WordScorePoS
size0.0368585655748969NN
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city0NN
increase0.0159575617614215NN
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improve0VB
environment0.0221298700466866NN
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environmental0.0221298700466866JJ
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The investments and strategic decisions made over the next few years will determine where the winners and losers will be in rising to the challenge of sustainable future

Label: 11, with a score of: 0.482094383909392
WordScorePoS
investment0NN
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Label: 13, with a score of: 0.417825013636453
WordScorePoS
investment0NN
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Label: 17, with a score of: 0.315772755046591
WordScorePoS
investment0.0766240489430275NN
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Further Reading Annex describes the World Bank Eco Cities Initiative which helps cities design development pathways for both ecological and economic sustainability

Label: 11, with a score of: 0.951857444780802
WordScorePoS
world0.00626002771693937NN
bank0NN
city0.607697021770652NN
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development0.0100241617629471NN
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Label: 8, with a score of: 0.101659297545229
WordScorePoS
world0.00172202218602071NN
bank0NN
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development0.0055149369084567NN
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WHY URBAN SUSTAINABILITY MATTERS PART II

Label: 11, with a score of: 0.966188480208834
WordScorePoS
urban0.27622591898666JJ
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The Path to Sustainability Photo Shutterstock BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Building Clean and Efficient Cities Key Messages ' Land and housing regulations as well as market based incentives can be used to encourage compact efficient cities

Label: 11, with a score of: 0.901478051817243
WordScorePoS
sustainability0NN
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regulation0NN
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Label: 17, with a score of: 0.288629026725896
WordScorePoS
sustainability0.036961556179878NN
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land0NN
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regulation0.036961556179878NN
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Label: 7, with a score of: 0.12323042068925
WordScorePoS
sustainability0NN
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world0.00261936653839306NN
clean0.125032076213024JJ
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' Rapid urbanization particularly in Africa the most rapidly urbanizing continent presents the risk of uncontrolled sprawl as well as the opportunity to transition directly to more sustainable infrastructure

Label: 11, with a score of: 0.649139771247427
WordScorePoS
rapid0.0373517915079663JJ
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africa0IN
rapidly0RB
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risk0.0631828116343571NN
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Label: 9, with a score of: 0.5485155401133
WordScorePoS
rapid0.030745604615229JJ
urbanization0NN
africa0IN
rapidly0RB
continent0NN
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opportunity0.0135141354781442NN
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' Cities are the primary global energy consumers

Label: 11, with a score of: 0.66713390136134
WordScorePoS
city0.607697021770652NN
primary0JJ
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energy0.0492535935240667NN
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Label: 7, with a score of: 0.52384397302673
WordScorePoS
city0NN
primary0JJ
global0.0251662980592747JJ
energy0.494617163780726NN
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Label: 12, with a score of: 0.485543273441015
WordScorePoS
city0NN
primary0JJ
global0.022306853314744JJ
energy0.133960988253756NN
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Both developed and developing country cities need to enact policies that increase energy efficiency and promote cleaner energy sources for electricity generation buildings and urban transport

Label: 7, with a score of: 0.665560577402215
WordScorePoS
develop0VB
country0NN
city0NN
policy0NN
increase0.0162235147372888NN
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promote0.00838876601975825VB
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electricity0.0625160381065121NN
generation0NN
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urban0JJ
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Label: 11, with a score of: 0.639112199436176
WordScorePoS
develop0VB
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policy0.0136980135278128NN
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building0.0747035830159326NN
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Label: 12, with a score of: 0.23498205438974
WordScorePoS
develop0VB
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policy0.0203215381458133NN
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cleaner0JJR
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generation0.0819583881728876NN
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Label: 17, with a score of: 0.159348152200528
WordScorePoS
develop0VB
country0NN
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policy0.0641521854453136NN
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' Buildings constitute the largest opportunity to improve demand side energy efficiency

Label: 7, with a score of: 0.820212062198443
WordScorePoS
building0NN
largest0JJS
opportunity0.0549574626423028NN
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energy0.494617163780726NN

Label: 17, with a score of: 0.256813763078271
WordScorePoS
building0.149940347231585NN
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energy0.0109843026967392NN

Label: 12, with a score of: 0.191923500170277
WordScorePoS
building0NN
largest0JJS
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improve0VB
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energy0.133960988253756NN

Green building standards target the operational phase of building life while embodied energy in buildings can be conserved through the adaptive reuse of historic built assets

Label: 17, with a score of: 0.557499996135157
WordScorePoS
green0JJ
building0.149940347231585NN
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target0.0898760357768464NN
phase0NN
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energy0.0109843026967392NN
conserve0VB
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build0.0499801157438617VB
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Label: 7, with a score of: 0.476532625965998
WordScorePoS
green0JJ
building0NN
standard0NN
target0NN
phase0NN
life0.0188084899376888NN
energy0.494617163780726NN
conserve0VB
adaptive0JJ
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Label: 12, with a score of: 0.355149963769819
WordScorePoS
green0.0332151194779746JJ
building0NN
standard0.0409791940864438NN
target0NN
phase0.108503934593034NN
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conserve0VB
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Label: 16, with a score of: 0.38106395490541
WordScorePoS
green0JJ
building0.109790895760528NN
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target0NN
phase0NN
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energy0NN
conserve0VB
adaptive0JJ
reuse0NN
build0VB
asset0.0811931365363091NN

Label: 11, with a score of: 0.305712028844785
WordScorePoS
green0.04477822029736JJ
building0.0747035830159326NN
standard0NN
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' Emissions from transport are likely to increase dramatically as demand for private transportation grows in the developing world

Label: 13, with a score of: 0.553354316195707
WordScorePoS
emission0.19598742448524NN
transport0NN
increase0.0145316075945746NN
demand0NN
private0JJ
transportation0NN
grow0.0171695137813101VB
develop0VB
world0.00351929966767313NN

Label: 11, with a score of: 0.452888255839061
WordScorePoS
emission0.0373517915079663NN
transport0.0747035830159326NN
increase0.00323105002784081NN
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transportation0.04477822029736NN
grow0.0229054731708393VB
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world0.00626002771693937NN

Label: 12, with a score of: 0.393810774877323
WordScorePoS
emission0.0554128417527413NN
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increase0.00958677785775002NN
demand0NN
private0JJ
transportation0.0332151194779746NN
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develop0VB
world0.00464349782489754NN

Label: 9, with a score of: 0.324864643706864
WordScorePoS
emission0NN
transport0.030745604615229NN
increase0.0159575617614215NN
demand0.030745604615229NN
private0.0368585655748969JJ
transportation0NN
grow0.0188543197850406VB
develop0VB
world0.00257642711761688NN

Transport sector investments need to provide viable alternative to automobile use

Label: 9, with a score of: 0.624748511486897
WordScorePoS
transport0.030745604615229NN
sector0.0565629593551219NN
investment0.0377086395700813NN
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alternative0.0602029956716497NN

Label: 17, with a score of: 0.498200198624022
WordScorePoS
transport0.0249900578719309NN
sector0.0459744293658165NN
investment0.0766240489430275NN
provide0.00601568197638796VB
alternative0NN

Label: 11, with a score of: 0.329782252642579
WordScorePoS
transport0.0747035830159326NN
sector0NN
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provide0.0269743071558856VB
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' Waste generation is increasing in quantity and complexity with urban growth

Label: 11, with a score of: 0.623066619427382
WordScorePoS
waste0.0373517915079663NN
generation0NN
increase0.00323105002784081NN
urban0.27622591898666JJ
growth0NN

Label: 12, with a score of: 0.452492118104872
WordScorePoS
waste0.138532104381853NN
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Label: 8, with a score of: 0.424169262413284
WordScorePoS
waste0NN
generation0NN
increase0.00711043475673939NN
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growth0.208565600657229NN

Label: 10, with a score of: 0.321574972027174
WordScorePoS
waste0NN
generation0NN
increase0.00285817158600878NN
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growth0.11178243319132NN

Municipal solid waste generation is unlikely to peak before and this will exacerbate shortfalls in municipal budgets to collect and properly dispose of waste

Label: 12, with a score of: 0.792590031462914
WordScorePoS
municipal0JJ
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Cities can take advantage of their massive growth in the coming decades to become more livable and sustainable but they will need to move quickly and target the sectors and policies that have the greatest influence on resource efficiency greenhouse gas emissions and other pollution

Label: 11, with a score of: 0.691627930349576
WordScorePoS
city0.607697021770652NN
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greenhouse0NN
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emission0.0373517915079663NN
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Label: 13, with a score of: 0.438490676240401
WordScorePoS
city0NN
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Label: 8, with a score of: 0.258368268689517
WordScorePoS
city0NN
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sustainable0.00688808874408283JJ
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Rapid growth will necessitate supply of serviced land and affordable housing embedded in city form that serves the economic needs of the community

Label: 11, with a score of: 0.793283746012923
WordScorePoS
rapid0.0373517915079663JJ
growth0NN
supply0NN
service0.0269743071558856NN
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housing0.219415728260093NN
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form0NN
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Label: 8, with a score of: 0.340452061961424
WordScorePoS
rapid0JJ
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service0.0197870712294258NN
land0NN
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city0NN
form0.0591643007226199NN
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Label: 16, with a score of: 0.129352503612351
WordScorePoS
rapid0JJ
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supply0NN
service0NN
land0NN
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city0NN
form0.158049144643351NN
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It will be necessary as well to invest in connective infrastructure and basic services that have the lowest possible resource intensity

Label: 9, with a score of: 0.568926187643086
WordScorePoS
invest0VB
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Label: 7, with a score of: 0.483946090776963
WordScorePoS
invest0VB
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Label: 1, with a score of: 0.379061184491614
WordScorePoS
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Label: 11, with a score of: 0.311458699002247
WordScorePoS
invest0VB
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An essential starting point for growing cities is the urban form shaped by land and housing policies

Label: 11, with a score of: 0.931122700474374
WordScorePoS
essential0JJ
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city0.607697021770652NN
urban0.27622591898666JJ
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land0.0389167984372011NN
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Label: 16, with a score of: 0.171945170393079
WordScorePoS
essential0.0395122861608378JJ
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form0.158049144643351NN
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As discussed in Chapter urban density or sprawl broadly affect efficiency and economic productivity

Label: 11, with a score of: 0.821198408742253
WordScorePoS
urban0.27622591898666JJ
density0.0731385760866977NN
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Label: 8, with a score of: 0.327147796672693
WordScorePoS
urban0JJ
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In addition three urban sectors energy buildings and transportation are responsible for the bulk of global greenhouse gas emissions and deserve priority in analytical inquiries and policy actions

Label: 7, with a score of: 0.622772354237707
WordScorePoS
addition0NN
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Label: 11, with a score of: 0.426150313297466
WordScorePoS
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Label: 13, with a score of: 0.36345329937748
WordScorePoS
addition0NN
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Label: 12, with a score of: 0.341751304229085
WordScorePoS
addition0NN
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Label: 17, with a score of: 0.257001339269025
WordScorePoS
addition0NN
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Urban electricity heat and cooling together contribute percent of global energy related emissions buildings contribute percent and urban transportation contributes percent WRI

Label: 11, with a score of: 0.631492291025337
WordScorePoS
urban0.27622591898666JJ
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Label: 7, with a score of: 0.525557526691765
WordScorePoS
urban0JJ
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Label: 12, with a score of: 0.341889464287283
WordScorePoS
urban0JJ
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Label: 13, with a score of: 0.214122504211395
WordScorePoS
urban0JJ
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Label: 17, with a score of: 0.208665022179077
WordScorePoS
urban0JJ
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Water and solid waste management are also central to sustainable cities

Label: 11, with a score of: 0.715045482291048
WordScorePoS
water0.0458109463416786NN
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central0JJ
sustainable0.00782503464617421JJ
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Label: 6, with a score of: 0.55969167860073
WordScorePoS
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Label: 12, with a score of: 0.35003248582658
WordScorePoS
water0.186896403054502NN
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Box reviews how the key areas could be addressed in an emerging economy like China and Figure shows the top actions taken by cities in the network

Label: 11, with a score of: 0.895931054621221
WordScorePoS
review0NN
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PART II THE PATH TO SUSTAINABILITY of Respondents FIG

Label: 12, with a score of: 0.742570353158457
WordScorePoS
sustainability0.0409791940864438NN

How Cities are Reducing Emissions Subsidies Fiscal Incentives Building Standards Awareness Consultation Infrastructure Urban Planning Transport Renewable Energies Retrofitting Waste Treatment Permitting Incentives District Heating Tree Planting Water Management Technical Solutions Source Adapted from CDP

Label: 11, with a score of: 0.611101897030196
WordScorePoS
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Label: 7, with a score of: 0.435048481005694
WordScorePoS
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plan0NN
transport0NN
renewable0.158624482267508JJ
energy0.494617163780726NN
waste0.0625160381065121NN
treatment0NN
heating0.122412709674631NN
tree0NN
plant0NN
water0NN
management0NN
technical0JJ
solution0NN
source0.0274787313211514NN

Label: 12, with a score of: 0.371295305423295
WordScorePoS
city0NN
reduce0.022306853314744VB
emission0.0554128417527413NN
subsidy0.0664302389559493NN
fiscal0JJ
incentive0NN
building0NN
standard0.0409791940864438NN
awareness0.0819583881728876NN
infrastructure0.028867295332595NN
urban0JJ
plan0.0144336476662975NN
transport0.0277064208763707NN
renewable0.0234335422829798JJ
energy0.133960988253756NN
waste0.138532104381853NN
treatment0NN
heating0NN
tree0NN
plant0NN
water0.186896403054502NN
management0.0339811641917276NN
technical0JJ
solution0NN
source0NN

Label: 6, with a score of: 0.348651030237174
WordScorePoS
city0NN
reduce0.00936547854413134VB
emission0NN
subsidy0NN
fiscal0JJ
incentive0NN
building0.0348974218717993NN
standard0NN
awareness0NN
infrastructure0.0181797964452808NN
urban0JJ
plan0NN
transport0NN
renewable0.0295155485671997JJ
energy0.0153390539205676NN
waste0NN
treatment0.0348974218717993NN
heating0NN
tree0NN
plant0NN
water0.556409482163189NN
management0.0428007293971684NN
technical0JJ
solution0NN
source0.0613562156822703NN

Label: 9, with a score of: 0.214253711866919
WordScorePoS
city0NN
reduce0VB
emission0NN
subsidy0NN
fiscal0JJ
incentive0NN
building0NN
standard0.0454743001434393NN
awareness0NN
infrastructure0.192202909044225NN
urban0JJ
plan0NN
transport0.030745604615229NN
renewable0.0520080474473126JJ
energy0.0540565419125769NN
waste0NN
treatment0NN
heating0NN
tree0NN
plant0NN
water0.0377086395700813NN
management0NN
technical0.030745604615229JJ
solution0.0368585655748969NN
source0.0135141354781442NN

Label: 13, with a score of: 0.174993747268494
WordScorePoS
city0NN
reduce0.00375696197697663VB
emission0.19598742448524NN
subsidy0NN
fiscal0JJ
incentive0NN
building0NN
standard0NN
awareness0.0414107553022249NN
infrastructure0NN
urban0JJ
plan0.0291713033871158NN
transport0NN
renewable0.0236803262479443JJ
energy0.0123065238088614NN
waste0NN
treatment0NN
heating0NN
tree0NN
plant0NN
water0NN
management0.0171695137813101NN
technical0JJ
solution0.0671298309154199NN
source0.0123065238088614NN

Label: 2, with a score of: 0.142115128655382
WordScorePoS
city0.0440683498519077NN
reduce0.00799614078871077VB
emission0NN
subsidy0.0357189919948307NN
fiscal0JJ
incentive0NN
building0NN
standard0NN
awareness0NN
infrastructure0.0155217067874935NN
urban0JJ
plan0NN
transport0NN
renewable0JJ
energy0.0130963126055467NN
waste0.0297950283197006NN
treatment0NN
heating0NN
tree0NN
plant0.132205049555723NN
water0NN
management0NN
technical0JJ
solution0.0357189919948307NN
source0.0130963126055467NN

Label: 14, with a score of: 0.13681085125259
WordScorePoS
city0NN
reduce0.0036456034155327VB
emission0NN
subsidy0.162850158165781NN
fiscal0JJ
incentive0NN
building0NN
standard0NN
awareness0NN
infrastructure0NN
urban0JJ
plan0.0141533244035107NN
transport0NN
renewable0JJ
energy0NN
waste0NN
treatment0.0271683202880942NN
heating0NN
tree0NN
plant0NN
water0.0333211986002325NN
management0.0499817979003487NN
technical0JJ
solution0NN
source0.0238835024181023NN

Label: 17, with a score of: 0.130386748606591
WordScorePoS
city0NN
reduce0.00335331148065841VB
emission0NN
subsidy0NN
fiscal0JJ
incentive0.036961556179878NN
building0.149940347231585NN
standard0NN
awareness0NN
infrastructure0.0130185595639838NN
urban0JJ
plan0.0130185595639838NN
transport0.0249900578719309NN
renewable0JJ
energy0.0109843026967392NN
waste0NN
treatment0NN
heating0NN
tree0NN
plant0NN
water0NN
management0NN
technical0JJ
solution0NN
source0.0109843026967392NN

Chinese cities have among the highest levels of per capita greenhouse emissions in the world

Label: 11, with a score of: 0.879303827260656
WordScorePoS
city0.607697021770652NN
level0.00692515656684925NN
capita0.04477822029736NN
greenhouse0NN
emission0.0373517915079663NN
world0.00626002771693937NN

Label: 13, with a score of: 0.385441445423701
WordScorePoS
city0NN
level0.0415277415054218NN
capita0NN
greenhouse0.0671298309154199NN
emission0.19598742448524NN
world0.00351929966767313NN

As millions of people migrate to cities over the next years China will need strategy to curb carbon emissions in urban areas

Label: 11, with a score of: 0.935415289263949
WordScorePoS
million0CD
people0.0161552501392041NNS
migrate0VB
city0.607697021770652NN
strategy0NN
carbon0.0373517915079663NN
emission0.0373517915079663NN
urban0.27622591898666JJ

Label: 13, with a score of: 0.272766621283456
WordScorePoS
million0CD
people0.0121096729954788NNS
migrate0VB
city0NN
strategy0.0201523636533764NN
carbon0.0559964069957828NN
emission0.19598742448524NN
urban0JJ

The following elements are essential building blocks of such strategy 'Increasing energy efficiency and use of clean energy sources Cities should make an effort to reduce carbon emissions by sustaining demand side energy efficiency measures particularly in industry power heating and buildings

Label: 7, with a score of: 0.795479761280101
WordScorePoS
essential0.0449973847138318JJ
building0NN
strategy0NN
energy0.494617163780726NN
clean0.125032076213024JJ
source0.0274787313211514NN
city0NN
effort0NN
reduce0.00838876601975825VB
carbon0.0625160381065121NN
emission0.0625160381065121NN
sustain0VB
demand0NN
measure0NN
industry0NN
power0NN
heating0.122412709674631NN

Label: 11, with a score of: 0.419883684526282
WordScorePoS
essential0JJ
building0.0747035830159326NN
strategy0NN
energy0.0492535935240667NN
clean0JJ
source0NN
city0.607697021770652NN
effort0.0229054731708393NN
reduce0.0200483235258943VB
carbon0.0373517915079663NN
emission0.0373517915079663NN
sustain0VB
demand0NN
measure0NN
industry0NN
power0NN
heating0NN

Label: 12, with a score of: 0.21058103920042
WordScorePoS
essential0JJ
building0NN
strategy0NN
energy0.133960988253756NN
clean0JJ
source0NN
city0NN
effort0NN
reduce0.022306853314744VB
carbon0NN
emission0.0554128417527413NN
sustain0.0332151194779746VB
demand0NN
measure0NN
industry0NN
power0NN
heating0NN

Label: 9, with a score of: 0.198501387853343
WordScorePoS
essential0JJ
building0NN
strategy0NN
energy0.0540565419125769NN
clean0.030745604615229JJ
source0.0135141354781442NN
city0NN
effort0.0188543197850406NN
reduce0VB
carbon0NN
emission0NN
sustain0VB
demand0.030745604615229NN
measure0NN
industry0.181897200573757NN
power0.0454743001434393NN
heating0NN

Label: 13, with a score of: 0.169727748763411
WordScorePoS
essential0JJ
building0NN
strategy0.0201523636533764NN
energy0.0123065238088614NN
clean0JJ
source0.0123065238088614NN
city0NN
effort0.0171695137813101NN
reduce0.00375696197697663VB
carbon0.0559964069957828NN
emission0.19598742448524NN
sustain0VB
demand0NN
measure0.0710409787438328NN
industry0NN
power0NN
heating0NN

Label: 17, with a score of: 0.165124613544997
WordScorePoS
essential0JJ
building0.149940347231585NN
strategy0.017987180284335NN
energy0.0109843026967392NN
clean0JJ
source0.0109843026967392NN
city0NN
effort0NN
reduce0.00335331148065841VB
carbon0NN
emission0NN
sustain0VB
demand0NN
measure0NN
industry0NN
power0.036961556179878NN
heating0NN

In addition cities could develop clean sources of energy supply with rooftop solar PV and solar water heating installations

Label: 7, with a score of: 0.594487703908264
WordScorePoS
addition0NN
city0NN
develop0VB
clean0.125032076213024JJ
source0.0274787313211514NN
energy0.494617163780726NN
supply0.0528748274225028NN
water0NN
heating0.122412709674631NN

Label: 6, with a score of: 0.525540113708257
WordScorePoS
addition0NN
city0NN
develop0VB
clean0.0348974218717993JJ
source0.0613562156822703NN
energy0.0153390539205676NN
supply0.0590310971343993NN
water0.556409482163189NN
heating0NN

Label: 11, with a score of: 0.507995171417531
WordScorePoS
addition0NN
city0.607697021770652NN
develop0VB
clean0JJ
source0NN
energy0.0492535935240667NN
supply0NN
water0.0458109463416786NN
heating0NN

Label: 12, with a score of: 0.282750786799458
WordScorePoS
addition0NN
city0NN
develop0VB
clean0JJ
source0NN
energy0.133960988253756NN
supply0.0703006268489394NN
water0.186896403054502NN
heating0NN

'Reducing transport sector emissions To minimize emissions from the transportation sector reduced motorization will be required

Label: 13, with a score of: 0.638411814323571
WordScorePoS
transport0NN
sector0NN
emission0.19598742448524NN
minimize0VB
transportation0NN
reduce0.00375696197697663VB
require0.0473606524958885VB

Label: 12, with a score of: 0.491795189529018
WordScorePoS
transport0.0277064208763707NN
sector0.0169905820958638NN
emission0.0554128417527413NN
minimize0.0664302389559493VB
transportation0.0332151194779746NN
reduce0.022306853314744VB
require0.0468670845659596VB

Label: 11, with a score of: 0.30866995645789
WordScorePoS
transport0.0747035830159326NN
sector0NN
emission0.0373517915079663NN
minimize0VB
transportation0.04477822029736NN
reduce0.0200483235258943VB
require0VB

Decisive action should be taken both to adopt new technologies and provide high quality public and non motorized transport

Label: 17, with a score of: 0.45697356207558
WordScorePoS
action0.0109843026967392NN
adopt0.0153248097886055VB
technology0.109843026967392NN
provide0.00601568197638796VB
quality0.00916459792075908NN
public0.0390556786919513NN
transport0.0249900578719309NN

Label: 11, with a score of: 0.417251484885187
WordScorePoS
action0NN
adopt0.0229054731708393VB
technology0NN
provide0.0269743071558856VB
quality0.0136980135278128NN
public0.0583751976558017NN
transport0.0747035830159326NN

Label: 5, with a score of: 0.412447362470104
WordScorePoS
action0.0409315617631NN
adopt0.0285529457667019VB
technology0.0409315617631NN
provide0.0112083245137075VB
quality0NN
public0.0727679293221751NN
transport0NN

Label: 9, with a score of: 0.363479970297196
WordScorePoS
action0.0135141354781442NN
adopt0VB
technology0.0810848128688654NN
provide0.00740117451847634VB
quality0.022550656390892NN
public0.0160169090870187NN
transport0.030745604615229NN

'Managing cities physical growth Cities need to intervene in the shape and direction of their physical growth

Label: 11, with a score of: 0.922382769111538
WordScorePoS
city0.607697021770652NN
growth0NN
direction0NN

Label: 8, with a score of: 0.316567811563557
WordScorePoS
city0NN
growth0.208565600657229NN
direction0NN

Cities with higher densities emit less greenhouse gases

Label: 11, with a score of: 0.953000690453553
WordScorePoS
city0.607697021770652NN
density0.0731385760866977NN
greenhouse0NN
gas0NN

Cities not only need to grow denser but also smarter fostering compact communities multiple use buildings and public transport networks

Label: 11, with a score of: 0.901103391569848
WordScorePoS
city0.607697021770652NN
grow0.0229054731708393VB
foster0JJ
community0NN
multiple0JJ
building0.0747035830159326NN
public0.0583751976558017NN
transport0.0747035830159326NN

Label: 17, with a score of: 0.32231439781597
WordScorePoS
city0NN
grow0VB
foster0.036961556179878JJ
community0NN
multiple0.048933054487825JJ
building0.149940347231585NN
public0.0390556786919513NN
transport0.0249900578719309NN

BOX Case Study Low Carbon Urban Development in China 'Support of low carbon lifestyles With rising income and higher individual purchasing power and consumption demands low carbon lifestyle will be key determinant of future energy demand in Chinese cities

Label: 11, with a score of: 0.678772749128783
WordScorePoS
study0NN
carbon0.0373517915079663NN
urban0.27622591898666JJ
development0.0100241617629471NN
lifestyle0NN
rise0.0268848280079943NN
income0NN
individual0JJ
power0NN
consumption0.08955644059472NN
demand0NN
key0NN
future0.0373517915079663NN
energy0.0492535935240667NN
city0.607697021770652NN

Label: 7, with a score of: 0.443333830819054
WordScorePoS
study0NN
carbon0.0625160381065121NN
urban0JJ
development0NN
lifestyle0NN
rise0NN
income0.0449973847138318NN
individual0JJ
power0NN
consumption0NN
demand0NN
key0.0625160381065121NN
future0NN
energy0.494617163780726NN
city0NN

Label: 12, with a score of: 0.427795069475874
WordScorePoS
study0NN
carbon0NN
urban0JJ
development0.022306853314744NN
lifestyle0.122937582259331NN
rise0NN
income0NN
individual0JJ
power0NN
consumption0.332151194779746NN
demand0NN
key0NN
future0.0277064208763707NN
energy0.133960988253756NN
city0NN

Label: 10, with a score of: 0.180527044426989
WordScorePoS
study0NN
carbon0NN
urban0.0488696285210061JJ
development0.0177346522508396NN
lifestyle0NN
rise0.0237821918091848NN
income0.166475342664294NN
individual0.064698038783335JJ
power0NN
consumption0NN
demand0NN
key0NN
future0NN
energy0NN
city0NN

Label: 8, with a score of: 0.0860795018975802
WordScorePoS
study0NN
carbon0NN
urban0JJ
development0.0055149369084567NN
lifestyle0NN
rise0NN
income0NN
individual0JJ
power0NN
consumption0.147812295323721NN
demand0NN
key0NN
future0NN
energy0NN
city0NN

Some tools have been developed internationally to engage citizens in understanding their household carbon emissions and taking action to reduce them

Label: 13, with a score of: 0.629479956063602
WordScorePoS
tool0NN
develop0VB
internationally0RB
engage0VB
household0NN
carbon0.0559964069957828NN
emission0.19598742448524NN
action0.0369195714265843NN
reduce0.00375696197697663VB

Label: 12, with a score of: 0.605520771435918
WordScorePoS
tool0.0542519672965169NN
develop0VB
internationally0RB
engage0.0819583881728876VB
household0.0554128417527413NN
carbon0NN
emission0.0554128417527413NN
action0.0121782716594324NN
reduce0.022306853314744VB

Similar partnerships at the city and neighborhood level in China could contribute to less carbon intensive households

Label: 11, with a score of: 0.790314807511277
WordScorePoS
partnership0NN
city0.607697021770652NN
level0.00692515656684925NN
contribute0VB
carbon0.0373517915079663NN
intensive0JJ
household0NN

Label: 17, with a score of: 0.521670095970463
WordScorePoS
partnership0.3914644359026NN
city0NN
level0.0138997399903163NN
contribute0.0249900578719309VB
carbon0NN
intensive0JJ
household0NN

'Replacing energy intensive manufacturing with low energy intensity economic activities Changes in the urban economic base such as transition to service industries will reduce emissions

Label: 7, with a score of: 0.725325993942363
WordScorePoS
energy0.494617163780726NN
intensive0JJ
manufacture0NN
intensity0.122412709674631NN
economic0JJ
activity0NN
urban0JJ
base0NN
service0.0451471467893167NN
industry0NN
reduce0.00838876601975825VB
emission0.0625160381065121NN

Label: 9, with a score of: 0.360206893782597
WordScorePoS
energy0.0540565419125769NN
intensive0JJ
manufacture0.240811982686599NN
intensity0NN
economic0.0320338181740375JJ
activity0NN
urban0JJ
base0NN
service0.0148023490369527NN
industry0.181897200573757NN
reduce0VB
emission0NN

Label: 11, with a score of: 0.317225442889106
WordScorePoS
energy0.0492535935240667NN
intensive0JJ
manufacture0NN
intensity0NN
economic0.0389167984372011JJ
activity0NN
urban0.27622591898666JJ
base0NN
service0.0269743071558856NN
industry0NN
reduce0.0200483235258943VB
emission0.0373517915079663NN

Label: 12, with a score of: 0.282874312300659
WordScorePoS
energy0.133960988253756NN
intensive0JJ
manufacture0NN
intensity0NN
economic0.0433009429988926JJ
activity0.0199423462679015NN
urban0JJ
base0.0199423462679015NN
service0.00666957305782837NN
industry0NN
reduce0.022306853314744VB
emission0.0554128417527413NN

Label: 8, with a score of: 0.231764375001999
WordScorePoS
energy0NN
intensive0.0804764811724088JJ
manufacture0NN
intensity0NN
economic0.128463821595706JJ
activity0.0295821503613099NN
urban0JJ
base0NN
service0.0197870712294258NN
industry0NN
reduce0.0055149369084567VB
emission0NN

Label: 13, with a score of: 0.144456673648129
WordScorePoS
energy0.0123065238088614NN
intensive0JJ
manufacture0NN
intensity0NN
economic0JJ
activity0.0201523636533764NN
urban0JJ
base0NN
service0NN
industry0NN
reduce0.00375696197697663VB
emission0.19598742448524NN

However such strategies need to be considered carefully

Label: 1, with a score of: 0.686656441771374
WordScorePoS
strategy0.0442083771593746NN

For today industrial centers simply relocating higher emission industries outside city boundary to reduce the greenhouse gas emissions of that city would make little if any difference on the national scale

Label: 11, with a score of: 0.829027747643067
WordScorePoS
industrial0JJ
emission0.0373517915079663NN
industry0NN
city0.607697021770652NN
reduce0.0200483235258943VB
greenhouse0NN
gas0NN
national0.00501208088147356JJ
scale0NN

Label: 13, with a score of: 0.367295920298483
WordScorePoS
industrial0.0414107553022249JJ
emission0.19598742448524NN
industry0NN
city0NN
reduce0.00375696197697663VB
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
national0.0112708859309299JJ
scale0NN

Label: 9, with a score of: 0.341153719082153
WordScorePoS
industrial0.318320101004075JJ
emission0NN
industry0.181897200573757NN
city0NN
reduce0VB
greenhouse0NN
gas0NN
national0.00412562425683033JJ
scale0.0368585655748969NN

However rapidly growing small and medium sized cities may have the opportunity to leap frog and bypass the polluting high carbon growth paths taken by the earlier generation of Chinese cities

Label: 11, with a score of: 0.919240230602023
WordScorePoS
rapidly0RB
grow0.0229054731708393VB
medium0NN
size0NN
city0.607697021770652NN
opportunity0.0164178645080222NN
pollute0VB
carbon0.0373517915079663NN
growth0NN
generation0NN

Label: 8, with a score of: 0.252389314175062
WordScorePoS
rapidly0RB
grow0VB
medium0.0607878664258118NN
size0.049270765107907NN
city0NN
opportunity0.0361300980868102NN
pollute0VB
carbon0NN
growth0.208565600657229NN
generation0NN

Source Reprinted from World Bank

Label: 2, with a score of: 0.737559496131694
WordScorePoS
source0.0130963126055467NN
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 6, with a score of: 0.426402661922482
WordScorePoS
source0.0613562156822703NN
world0.00438651305628668NN
bank0NN

Label: 17, with a score of: 0.33813808928791
WordScorePoS
source0.0109843026967392NN
world0.00418824502414673NN
bank0.036961556179878NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Land Management and Policy The supply of affordable serviced land is probably the most important input for sustainable urbanization

Label: 11, with a score of: 0.551065026914407
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
land0.0389167984372011NN
management0.0687164195125178NN
policy0.0136980135278128NN
supply0NN
service0.0269743071558856NN
input0NN
sustainable0.00782503464617421JJ
urbanization0.146277152173395NN

Label: 17, with a score of: 0.348108551410356
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
land0NN
management0NN
policy0.0641521854453136NN
supply0NN
service0NN
input0NN
sustainable0.0115176738164035JJ
urbanization0NN

Label: 15, with a score of: 0.442597825360761
WordScorePoS
building0NN
sustainability0NN
world0.00110699520004297NN
land0.123873694868802NN
management0.0486060126274891NN
policy0NN
supply0.0223459295604043NN
service0.0127200410376242NN
input0NN
sustainable0.00664197120025784JJ
urbanization0NN

The World Development Report World Bank explicitly mentioned the importance of good land markets to enable the effective expansion of urban agglomerations and the mobility of production factors

Label: 11, with a score of: 0.649797583197612
WordScorePoS
world0.00626002771693937NN
development0.0100241617629471NN
report0NN
bank0NN
land0.0389167984372011NN
market0NN
enable0.0373517915079663VB
expansion0.0731385760866977NN
urban0.27622591898666JJ
mobility0NN
production0NN

Label: 2, with a score of: 0.466615036005826
WordScorePoS
world0.0124838525528648NN
development0.0159922815774215NN
report0NN
bank0.0881366997038154NN
land0.0465651203624806NN
market0.0913569596328122NN
enable0VB
expansion0NN
urban0JJ
mobility0NN
production0.0548141757796873NN

Label: 12, with a score of: 0.323220855001283
WordScorePoS
world0.00464349782489754NN
development0.022306853314744NN
report0.0409791940864438NN
bank0NN
land0.0144336476662975NN
market0.0169905820958638NN
enable0VB
expansion0NN
urban0JJ
mobility0NN
production0.118934074671047NN

For countries in the earlier stages of urbanization land management is particularly important as records and titles are often missing legal systems are fragmented and inconsistent and private interests may lead to speculation and corruption as urban expansion increases demand for usable land

Label: 11, with a score of: 0.70409389208127
WordScorePoS
country0NN
stage0NN
urbanization0.146277152173395NN
land0.0389167984372011NN
management0.0687164195125178NN
record0NN
legal0JJ
system0.0315914058171786NN
private0JJ
lead0NN
corruption0NN
urban0.27622591898666JJ
expansion0.0731385760866977NN
increase0.00323105002784081NN
demand0NN

Label: 16, with a score of: 0.419811764267372
WordScorePoS
country0NN
stage0NN
urbanization0NN
land0NN
management0NN
record0NN
legal0.0811931365363091JJ
system0NN
private0JJ
lead0NN
corruption0.322472475577062NN
urban0JJ
expansion0NN
increase0NN
demand0NN

Tenure reform or systematizing land titles can bring major benefits in such cases Box

Label: 15, with a score of: 0.583509408328032
WordScorePoS
reform0NN
land0.123873694868802NN
bring0VB
major0.0137637438743113JJ

Label: 5, with a score of: 0.489350159151678
WordScorePoS
reform0.0911713013253052NN
land0.024255976440725NN
bring0VB
major0JJ

Label: 11, with a score of: 0.475055085923618
WordScorePoS
reform0NN
land0.0389167984372011NN
bring0.0731385760866977VB
major0JJ

Typical regulations that affect land availability include zoning minimum lot size floor area ratios and height limits

Label: 1, with a score of: 0.491492294642769
WordScorePoS
regulation0NN
land0.0319966432858631NN
availability0NN
include0VB
size0NN
ratio0NN
height0.120266261535276NN
limit0NN

Often land or housing regulations become constraints to quick and responsive supply of urban land

Label: 11, with a score of: 0.824081908733875
WordScorePoS
land0.0389167984372011NN
housing0.219415728260093NN
regulation0NN
constraint0NN
responsive0JJ
supply0NN
urban0.27622591898666JJ

Label: 15, with a score of: 0.388123149839658
WordScorePoS
land0.123873694868802NN
housing0NN
regulation0NN
constraint0NN
responsive0JJ
supply0.0223459295604043NN
urban0JJ

Minimum lot sizes minimum frontage and the percentage BOX To ensure the design of good local development plan cities need knowledge commitment to sustainability and an understanding of how density infrastructure and the use of transport alterna tives contribute to emissions and sustainability

Label: 11, with a score of: 0.781455873882495
WordScorePoS
size0NN
percentage0NN
ensure0.00501208088147356VB
local0.0194583992186006JJ
development0.0100241617629471NN
plan0.0583751976558017NN
city0.607697021770652NN
knowledge0NN
commitment0NN
sustainability0NN
density0.0731385760866977NN
infrastructure0.0194583992186006NN
transport0.0747035830159326NN
contribute0VB
emission0.0373517915079663NN

Label: 9, with a score of: 0.256339796012026
WordScorePoS
size0.0368585655748969NN
percentage0NN
ensure0.00412562425683033VB
local0JJ
development0.0330049940546426NN
plan0NN
city0NN
knowledge0NN
commitment0NN
sustainability0NN
density0NN
infrastructure0.192202909044225NN
transport0.030745604615229NN
contribute0VB
emission0NN

Label: 13, with a score of: 0.242715056106038
WordScorePoS
size0NN
percentage0NN
ensure0VB
local0.0145856516935579JJ
development0NN
plan0.0291713033871158NN
city0NN
knowledge0NN
commitment0.0414107553022249NN
sustainability0NN
density0NN
infrastructure0NN
transport0NN
contribute0VB
emission0.19598742448524NN

To implement such plan local authorities need to have good land records titles policies and the authority to allocate land and establish the rules as manifested in zoning laws adequate floor area ratios and height limits and building codes

Label: 17, with a score of: 0.48407238151911
WordScorePoS
implement0.0458229896037954VB
plan0.0130185595639838NN
local0.0130185595639838JJ
land0NN
record0.048933054487825NN
policy0.0641521854453136NN
establish0.048933054487825VB
rule0.0739231123597559NN
law0NN
adequate0JJ
ratio0NN
height0NN
limit0NN
building0.149940347231585NN

Label: 16, with a score of: 0.483282881803162
WordScorePoS
implement0VB
plan0NN
local0JJ
land0NN
record0NN
policy0.0201317944152378NN
establish0VB
rule0.162386273072618NN
law0.164686343640793NN
adequate0JJ
ratio0NN
height0NN
limit0NN
building0.109790895760528NN

Label: 11, with a score of: 0.35389744289539
WordScorePoS
implement0.0273960270556257VB
plan0.0583751976558017NN
local0.0194583992186006JJ
land0.0389167984372011NN
record0NN
policy0.0136980135278128NN
establish0VB
rule0NN
law0NN
adequate0.0631828116343571JJ
ratio0NN
height0NN
limit0NN
building0.0747035830159326NN

Label: 1, with a score of: 0.345075121888307
WordScorePoS
implement0.0450489732120807VB
plan0NN
local0JJ
land0.0319966432858631NN
record0NN
policy0.0450489732120807NN
establish0VB
rule0NN
law0NN
adequate0.0519476926891779JJ
ratio0NN
height0.120266261535276NN
limit0NN
building0NN

Thus the key pillars of urban planning include good land records and titling good understanding of how the city is growing the preferences of the residents and businesses and knowledge of how zoning and transport systems can work together to enable the implementation of the plan Box

Label: 11, with a score of: 0.901565359099659
WordScorePoS
key0NN
urban0.27622591898666JJ
plan0.0583751976558017NN
include0VB
land0.0389167984372011NN
record0NN
city0.607697021770652NN
grow0.0229054731708393VB
business0NN
knowledge0NN
transport0.0747035830159326NN
system0.0315914058171786NN
enable0.0373517915079663VB
implementation0NN

Cities would be best advised to understand how to deploy market incentives to promote growth in the desired direction Box

Label: 11, with a score of: 0.845886283465949
WordScorePoS
city0.607697021770652NN
market0NN
incentive0NN
promote0VB
growth0NN
direction0NN

Label: 8, with a score of: 0.363778591870527
WordScorePoS
city0NN
market0.0252035516550537NN
incentive0NN
promote0.0275746845422835VB
growth0.208565600657229NN
direction0NN

Case Study Systematizing Land Titles in Africa Africa used to be the continent where land systems were complex and where standardized title systems were usually absent

Label: 15, with a score of: 0.614676264865593
WordScorePoS
study0.0517339898971162NN
land0.123873694868802NN
africa0IN
continent0NN
system0NN

Label: 2, with a score of: 0.591929738793541
WordScorePoS
study0NN
land0.0465651203624806NN
africa0IN
continent0.0440683498519077NN
system0.0756001353333673NN

In the last few years however progress has been remarkable

Label: 1, with a score of: 0.796186976371871
WordScorePoS
progress0NN
remarkable0.120266261535276JJ

Label: 8, with a score of: 0.39167963733272
WordScorePoS
progress0.0591643007226199NN
remarkable0JJ

Several African countries have made impressive progress in recognizing traditional and modern titles and in improving land records and transaction deeds

Label: 7, with a score of: 0.848573343780738
WordScorePoS
african0IN
country0NN
progress0NN
recognize0VB
traditional0JJ
modern0.462321869452858JJ
improve0.0229264916384433VB
land0.0325677023224526NN
record0NN
transaction0NN

This not only offers solid basis for property taxation but provides the public sector with the fundamental tools for landuse planning and urban infrastructure development

Label: 11, with a score of: 0.576909142261067
WordScorePoS
offer0NN
basis0NN
property0NN
taxation0NN
public0.0583751976558017NN
sector0NN
fundamental0JJ
tool0NN
plan0.0583751976558017NN
urban0.27622591898666JJ
infrastructure0.0194583992186006NN
development0.0100241617629471NN

Label: 9, with a score of: 0.46875811808917
WordScorePoS
offer0.0454743001434393NN
basis0NN
property0NN
taxation0NN
public0.0160169090870187NN
sector0.0565629593551219NN
fundamental0JJ
tool0NN
plan0NN
urban0JJ
infrastructure0.192202909044225NN
development0.0330049940546426NN

Label: 5, with a score of: 0.379994884716234
WordScorePoS
offer0NN
basis0NN
property0.0558185411034276NN
taxation0NN
public0.0727679293221751NN
sector0.0571058915334038NN
fundamental0.0558185411034276JJ
tool0NN
plan0NN
urban0JJ
infrastructure0.024255976440725NN
development0.0124956749436837NN

Label: 12, with a score of: 0.300390133794845
WordScorePoS
offer0NN
basis0NN
property0NN
taxation0.0542519672965169NN
public0.028867295332595NN
sector0.0169905820958638NN
fundamental0JJ
tool0.0542519672965169NN
plan0.0144336476662975NN
urban0JJ
infrastructure0.028867295332595NN
development0.022306853314744NN

Photo Julianne Baker Gallegos World Bank Photo Jorge Láscar BOX Connecting Transportation and Land Use Planning Successful cities such as Seoul and Curitiba have promoted urban development around public transportation and amenities Curitiba or around urban core areas Seoul relying on transportation linkages mixed land uses and high quality urban services

Label: 11, with a score of: 0.92918439635191
WordScorePoS
world0.00626002771693937NN
bank0NN
transportation0.04477822029736NN
land0.0389167984372011NN
plan0.0583751976558017NN
successful0JJ
city0.607697021770652NN
promote0VB
urban0.27622591898666JJ
development0.0100241617629471NN
public0.0583751976558017NN
rely0VB
quality0.0136980135278128NN
service0.0269743071558856NN

Landuse zoning policies that allow for higher densities and greater mixing of residential and commercial uses enhance transportation goals by reducing trip distances while strategic mass transit linkages can attract development and promote compact growth

Label: 8, with a score of: 0.487703988069995
WordScorePoS
policy0.0452169560888236NN
density0NN
mix0NN
residential0JJ
commercial0JJ
enhance0.0123650647908625VB
transportation0NN
goal0NN
reduce0.0055149369084567VB
trip0NN
attract0VB
development0.0055149369084567NN
promote0.0275746845422835VB
growth0.208565600657229NN

Label: 10, with a score of: 0.42277700102169
WordScorePoS
policy0.0848203862485078NN
density0NN
mix0NN
residential0JJ
commercial0JJ
enhance0.00994073584907126VB
transportation0NN
goal0NN
reduce0.0310356414389692VB
trip0NN
attract0VB
development0.0177346522508396NN
promote0.00886732612541978VB
growth0.11178243319132NN

Label: 17, with a score of: 0.373825264400788
WordScorePoS
policy0.0641521854453136NN
density0NN
mix0NN
residential0JJ
commercial0JJ
enhance0.0601477977507998VB
transportation0NN
goal0NN
reduce0.00335331148065841VB
trip0NN
attract0.048933054487825VB
development0.0469463607292177NN
promote0.0100599344419752VB
growth0NN

Label: 12, with a score of: 0.354513909032291
WordScorePoS
policy0.0203215381458133NN
density0NN
mix0NN
residential0.0542519672965169JJ
commercial0.0542519672965169JJ
enhance0VB
transportation0.0332151194779746NN
goal0NN
reduce0.022306853314744VB
trip0NN
attract0VB
development0.022306853314744NN
promote0.0148712355431627VB
growth0NN

However density can also be perceived as cost crime and violence tend to be higher in dense places and local traffic is worse

Label: 16, with a score of: 0.930369825177363
WordScorePoS
density0NN
cost0.0548954478802642NN
crime0.214981650384708NN
violence0.164686343640793NN
local0JJ

These costs must be outweighed by the benefits of agglomeration and urban amenities including proximity to high quality public transportation Cheshire and Magrini

Label: 11, with a score of: 0.84921785748651
WordScorePoS
cost0NN
urban0.27622591898666JJ
include0VB
quality0.0136980135278128NN
public0.0583751976558017NN
transportation0.04477822029736NN

Label: 10, with a score of: 0.311711959801786
WordScorePoS
cost0.0660824365173543NN
urban0.0488696285210061JJ
include0VB
quality0.0121171980355011NN
public0.0172128079963482NN
transportation0NN

Long term growth plans aim to strike this balance in number of OECD metropolitan areas including London New York and Paris

Label: 8, with a score of: 0.59325005284755
WordScorePoS
term0NN
growth0.208565600657229NN
plan0NN
aim0.0410992516792148NN
oecd0NN
include0VB
paris0NN

Label: 17, with a score of: 0.446252452239253
WordScorePoS
term0.149793392961411NN
growth0NN
plan0.0130185595639838NN
aim0.0249900578719309NN
oecd0NN
include0VB
paris0NN

Label: 10, with a score of: 0.441539622601144
WordScorePoS
term0.0396106020715138NN
growth0.11178243319132NN
plan0.0344256159926965NN
aim0NN
oecd0NN
include0VB
paris0NN

Label: 12, with a score of: 0.357958126604816
WordScorePoS
term0NN
growth0NN
plan0.0144336476662975NN
aim0.0277064208763707NN
oecd0.108503934593034NN
include0VB
paris0NN

Photo Shutterstock BUILDING SUSTAINABILITY IN AN URBANIZING WORLD of plots allocated to public infrastructure effectively limit the supply of serviced land

Label: 9, with a score of: 0.434031610399129
WordScorePoS
building0NN
sustainability0NN
world0.00257642711761688NN
public0.0160169090870187NN
infrastructure0.192202909044225NN
limit0.0368585655748969NN
supply0NN
service0.0148023490369527NN
land0NN

Label: 17, with a score of: 0.402126695280327
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
public0.0390556786919513NN
infrastructure0.0130185595639838NN
limit0NN
supply0NN
service0NN
land0NN

Label: 11, with a score of: 0.371572375727806
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
public0.0583751976558017NN
infrastructure0.0194583992186006NN
limit0NN
supply0NN
service0.0269743071558856NN
land0.0389167984372011NN

Label: 7, with a score of: 0.328006902544671
WordScorePoS
building0NN
sustainability0NN
world0.00261936653839306NN
public0NN
infrastructure0.0651354046449052NN
limit0NN
supply0.0528748274225028NN
service0.0451471467893167NN
land0.0325677023224526NN

Label: 12, with a score of: 0.322081085564008
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
public0.028867295332595NN
infrastructure0.028867295332595NN
limit0NN
supply0.0703006268489394NN
service0.00666957305782837NN
land0.0144336476662975NN

In highly urbanized countries land policies continue to shape environmental social and economic outcomes

Label: 8, with a score of: 0.394452745927827
WordScorePoS
highly0RB
country0NN
land0NN
policy0.0452169560888236NN
continue0.0295821503613099VB
environmental0.0295821503613099JJ
social0.0504071033101074JJ
economic0.128463821595706JJ
outcome0NN

Label: 10, with a score of: 0.515274844349231
WordScorePoS
highly0RB
country0NN
land0NN
policy0.0848203862485078NN
continue0.0237821918091848VB
environmental0.0237821918091848JJ
social0.101310366625194JJ
economic0.103276847978089JJ
outcome0.0330412182586772NN

Label: 1, with a score of: 0.459885273796583
WordScorePoS
highly0RB
country0NN
land0.0319966432858631NN
policy0.0450489732120807NN
continue0VB
environmental0.0442083771593746JJ
social0.112994637345193JJ
economic0.0959899298575892JJ
outcome0NN

Label: 11, with a score of: 0.341018822806458
WordScorePoS
highly0RB
country0NN
land0.0389167984372011NN
policy0.0136980135278128NN
continue0.0537696560159885VB
environmental0.0537696560159885JJ
social0.0458109463416786JJ
economic0.0389167984372011JJ
outcome0NN

Label: 5, with a score of: 0.33394472921789
WordScorePoS
highly0RB
country0NN
land0.024255976440725NN
policy0.0341506708422088NN
continue0.0335134328085675VB
environmental0JJ
social0.0285529457667019JJ
economic0.0727679293221751JJ
outcome0.0465610847355851NN

Misguided regulations or inelastic land supply often lead to unaffordable land and housing prices in the center of the city pushing out the working class and low income households

Label: 11, with a score of: 0.815284338976031
WordScorePoS
regulation0NN
land0.0389167984372011NN
supply0NN
lead0NN
housing0.219415728260093NN
price0NN
city0.607697021770652NN
class0NN
income0NN
household0NN

Label: 10, with a score of: 0.267803736525567
WordScorePoS
regulation0.0977392570420122NN
land0NN
supply0NN
lead0NN
housing0NN
price0NN
city0NN
class0NN
income0.166475342664294NN
household0.0330412182586772NN

Label: 2, with a score of: 0.313532777876839
WordScorePoS
regulation0NN
land0.0465651203624806NN
supply0NN
lead0NN
housing0NN
price0.0583416713841148NN
city0.0440683498519077NN
class0.0583416713841148NN
income0.0643370113878709NN
household0.0297950283197006NN

Lack of sufficient transportation compromises the livelihoods of new urban residents

Label: 11, with a score of: 0.868965621103798
WordScorePoS
lack0.0229054731708393NN
transportation0.04477822029736NN
livelihood0NN
urban0.27622591898666JJ

Label: 1, with a score of: 0.321595341290789
WordScorePoS
lack0.0753297582301286NN
transportation0NN
livelihood0.0519476926891779NN
urban0JJ

While zoning and other regulations are necessary to preserve the planned use of land the usual result is to push residents to the urban periphery which eventually leads to sprawl

Label: 11, with a score of: 0.793710609940549
WordScorePoS
regulation0NN
plan0.0583751976558017NN
land0.0389167984372011NN
result0NN
urban0.27622591898666JJ
lead0NN

Label: 10, with a score of: 0.384691065271726
WordScorePoS
regulation0.0977392570420122NN
plan0.0344256159926965NN
land0NN
result0NN
urban0.0488696285210061JJ
lead0NN

Label: 15, with a score of: 0.326902654916895
WordScorePoS
regulation0NN
plan0.0137637438743113NN
land0.123873694868802NN
result0NN
urban0JJ
lead0.016202004209163NN

Recognizing this many cities have started working to reverse the constraints that bring about sprawl and decentralization

Label: 11, with a score of: 0.980911985860215
WordScorePoS
recognize0VB
city0.607697021770652NN
reverse0NN
constraint0NN
bring0.0731385760866977VB

In this vein it is also crucial that urban planning take account of informality

Label: 11, with a score of: 0.912126270942367
WordScorePoS
crucial0JJ
urban0.27622591898666JJ
plan0.0583751976558017NN
account0.0194583992186006NN

Due to lack of information commitment or knowledge many developing cities plan the official city and neglect Market Based Incentives for Land Policy BOX Market based incentives and regulatory frameworks are the key ingredients of good land policy

Label: 11, with a score of: 0.789713703486363
WordScorePoS
due0JJ
lack0.0229054731708393NN
information0NN
commitment0NN
knowledge0NN
develop0VB
city0.607697021770652NN
plan0.0583751976558017NN
neglect0NN
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base0NN
incentive0NN
land0.0389167984372011NN
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framework0.0194583992186006NN
key0NN

In the case of development density market based incentives should begin by dismantling old regulations that promoted sprawl

Label: 17, with a score of: 0.529188163746129
WordScorePoS
development0.0469463607292177NN
density0NN
market0.030649619577211NN
base0.017987180284335NN
incentive0.036961556179878NN
regulation0.036961556179878NN
promote0.0100599344419752VB

Label: 10, with a score of: 0.426151225983321
WordScorePoS
development0.0177346522508396NN
density0NN
market0.0202620733250388NN
base0NN
incentive0NN
regulation0.0977392570420122NN
promote0.00886732612541978VB

Label: 2, with a score of: 0.339927048540919
WordScorePoS
development0.0159922815774215NN
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market0.0913569596328122NN
base0NN
incentive0NN
regulation0NN
promote0.00799614078871077VB

state of South Carolina nonessential regulatory requirements on housinga were eliminated in order to encourage affordable housing traditional neighborhood design and density

Label: 11, with a score of: 0.848250090473336
WordScorePoS
south0RB
eliminate0VB
encourage0VB
affordable0.0458109463416786JJ
housing0.219415728260093NN
traditional0JJ
density0.0731385760866977NN

These market based incentives included density bonuses to promote densities higher than typically permitted relaxed zoning regulations regarding lot area requirements minimum setbacks yard requirements variances parking requirements and street layout reduced or waived fees including fees levied on new development streamlining and expediting the permitting process and traditional neighborhood design to promote high density and mixed use development

Label: 0, with a score of: 1

Transferable development rights TDR programs operate through the transfer of development rights from one geographic area to another within region

Label: 1, with a score of: 0.612607453477533
WordScorePoS
development0.0164833460638534NN
rights0.0519476926891779NNS
operate0VB
transfer0NN
geographic0JJ
region0.184259548107002NN

Label: 5, with a score of: 0.437007529041933
WordScorePoS
development0.0124956749436837NN
rights0.078760887431929NNS
operate0VB
transfer0NN
geographic0JJ
region0.0465610847355851NN

Label: 17, with a score of: 0.342982380869064
WordScorePoS
development0.0469463607292177NN
rights0NNS
operate0VB
transfer0.036961556179878NN
geographic0.048933054487825JJ
region0NN

Label: 3, with a score of: 0.337253726966537
WordScorePoS
development0.00837573444202498NN
rights0.0175975629039954NNS
operate0VB
transfer0NN
geographic0JJ
region0.124837844401518NN

For example local government may adopt zoning ordinance that assigns density of dwelling unit per acres zoning to rural area and density of dwelling unit per acre zoning to urban areas

Label: 11, with a score of: 0.904129492612151
WordScorePoS
local0.0194583992186006JJ
government0NN
adopt0.0229054731708393VB
density0.0731385760866977NN
rural0.0373517915079663JJ
urban0.27622591898666JJ

Label: 2, with a score of: 0.329032242018018
WordScorePoS
local0.0155217067874935JJ
government0NN
adopt0.0182713919265625VB
density0NN
rural0.148975141598503JJ
urban0JJ

If the local government wished to shift future growth from rural areas to urban areas it could downzone or decrease the density in the rural areas for example from zoning to zoning and upzone or increase the density in the urban areas for example from zoning to zoning

Label: 11, with a score of: 0.854370242923082
WordScorePoS
local0.0194583992186006JJ
government0NN
future0.0373517915079663NN
growth0NN
rural0.0373517915079663JJ
urban0.27622591898666JJ
decrease0.0731385760866977NN
density0.0731385760866977NN
increase0.00323105002784081NN

Label: 2, with a score of: 0.359378959477157
WordScorePoS
local0.0155217067874935JJ
government0NN
future0.0297950283197006NN
growth0.0252000451111224NN
rural0.148975141598503JJ
urban0JJ
decrease0NN
density0NN
increase0.0128868286091919NN

Label: 8, with a score of: 0.223424916844275
WordScorePoS
local0.0214106369326177JJ
government0NN
future0NN
growth0.208565600657229NN
rural0JJ
urban0JJ
decrease0NN
density0NN
increase0.00711043475673939NN

Under TDR framework the private market drives the shift in density once the local government adopts an ordinance allowing urban developers to purchase development rights from rural landowners

Label: 11, with a score of: 0.655227806891004
WordScorePoS
framework0.0194583992186006NN
private0JJ
market0NN
drive0NN
density0.0731385760866977NN
local0.0194583992186006JJ
government0NN
adopt0.0229054731708393VB
urban0.27622591898666JJ
development0.0100241617629471NN
rights0NNS
rural0.0373517915079663JJ

Label: 2, with a score of: 0.414540984009101
WordScorePoS
framework0NN
private0JJ
market0.0913569596328122NN
drive0NN
density0NN
local0.0155217067874935JJ
government0NN
adopt0.0182713919265625VB
urban0JJ
development0.0159922815774215NN
rights0NNS
rural0.148975141598503JJ

Label: 17, with a score of: 0.368316259509162
WordScorePoS
framework0.0130185595639838NN
private0.0898760357768464JJ
market0.030649619577211NN
drive0NN
density0NN
local0.0130185595639838JJ
government0.048933054487825NN
adopt0.0153248097886055VB
urban0JJ
development0.0469463607292177NN
rights0NNS
rural0JJ

Technically the urban developer is paying the rural landowner to place permanent conservation easement on his or her property in exchange for the ability to develop at higher densities in the urban area

Label: 11, with a score of: 0.896231542752818
WordScorePoS
urban0.27622591898666JJ
pay0.0315914058171786NN
rural0.0373517915079663JJ
conservation0NN
property0NN
ability0NN
develop0VB
density0.0731385760866977NN

Label: 2, with a score of: 0.192238674699075
WordScorePoS
urban0JJ
pay0NN
rural0.148975141598503JJ
conservation0NN
property0NN
ability0NN
develop0VB
density0NN

The amount paid is governed by the free market but generally should reflect the difference between the value of the rural property with development rights and without them

Label: 2, with a score of: 0.501342471133781
WordScorePoS
amount0NN
pay0NN
free0JJ
market0.0913569596328122NN
rural0.148975141598503JJ
property0NN
development0.0159922815774215NN
rights0NNS

Label: 5, with a score of: 0.441710509603506
WordScorePoS
amount0NN
pay0.078760887431929NN
free0JJ
market0NN
rural0JJ
property0.0558185411034276NN
development0.0124956749436837NN
rights0.078760887431929NNS

Label: 1, with a score of: 0.397989469080005
WordScorePoS
amount0.0614198493690006NN
pay0NN
free0JJ
market0NN
rural0JJ
property0.0736315832425121NN
development0.0164833460638534NN
rights0.0519476926891779NNS

Label: 17, with a score of: 0.327556955249142
WordScorePoS
amount0NN
pay0NN
free0.0898760357768464JJ
market0.030649619577211NN
rural0JJ
property0NN
development0.0469463607292177NN
rights0NNS

In this way the urban developer can secure greater densities in urban markets while the rural landowner can continue to use his or her property for traditional rural uses and receive payment for development rights without actually developing the property

Label: 11, with a score of: 0.73331563962036
WordScorePoS
urban0.27622591898666JJ
secure0JJ
density0.0731385760866977NN
market0NN
rural0.0373517915079663JJ
continue0.0537696560159885VB
property0NN
traditional0JJ
receive0VB
development0.0100241617629471NN
rights0NNS
develop0VB

Label: 2, with a score of: 0.473564024004982
WordScorePoS
urban0JJ
secure0.0440683498519077JJ
density0NN
market0.0913569596328122NN
rural0.148975141598503JJ
continue0VB
property0NN
traditional0.0440683498519077JJ
receive0VB
development0.0159922815774215NN
rights0NNS
develop0VB

The TDR program in Montgomery County Maryland viewed as one of the most successful in the United States has preserved nearly acres of land through market based TDR program

Label: 17, with a score of: 0.564771688598903
WordScorePoS
view0.048933054487825NN
successful0.048933054487825JJ
unite0.036961556179878VB
land0NN
market0.030649619577211NN
base0.017987180284335NN

Nonessential housing regulatory requirements may include requirements like minimum lot size setbacks open space landscaping impervious surfaces and parking

Label: 11, with a score of: 0.920228277760578
WordScorePoS
housing0.219415728260093NN
include0VB
size0NN
space0.04477822029736NN
surface0NN

PART II THE PATH TO SUSTAINABILITY Urban Population millions Percent Urban FIG

Label: 11, with a score of: 0.939962052137198
WordScorePoS
sustainability0NN
urban0.27622591898666JJ
population0.0194583992186006NN
million0CD

Label: 10, with a score of: 0.30208958158945
WordScorePoS
sustainability0NN
urban0.0488696285210061JJ
population0.0860640399817412NN
million0CD

Urban Population Percent Urban Source Adapted from UNDESA

Label: 11, with a score of: 0.929144253306542
WordScorePoS
urban0.27622591898666JJ
population0.0194583992186006NN
source0NN

the spontaneous growth that happens outside the administrative boundary

Label: 8, with a score of: 0.851431893492884
WordScorePoS
growth0.208565600657229NN

Label: 10, with a score of: 0.456331861301254
WordScorePoS
growth0.11178243319132NN

This leads to an actual growth of the metropolitan area that is outside the control or the supervision of specific urban authorities

Label: 11, with a score of: 0.623856166430183
WordScorePoS
lead0NN
actual0JJ
growth0NN
control0NN
urban0.27622591898666JJ

Label: 8, with a score of: 0.584889784781289
WordScorePoS
lead0.0504071033101074NN
actual0JJ
growth0.208565600657229NN
control0NN
urban0JJ

Label: 10, with a score of: 0.36283263973428
WordScorePoS
lead0NN
actual0JJ
growth0.11178243319132NN
control0NN
urban0.0488696285210061JJ

Only later when the pressure for infrastructure services arises is the city administration forced to deal with the massive growth that has happened outside its jurisdiction

Label: 11, with a score of: 0.794061150910311
WordScorePoS
pressure0.055245183797332NN
infrastructure0.0194583992186006NN
service0.0269743071558856NN
arise0VB
city0.607697021770652NN
force0NN
growth0NN
happen0VB

Label: 9, with a score of: 0.395606517542092
WordScorePoS
pressure0NN
infrastructure0.192202909044225NN
service0.0148023490369527NN
arise0VB
city0NN
force0NN
growth0.0260040237236563NN
happen0.120405991343299VB

Label: 8, with a score of: 0.30161949758813
WordScorePoS
pressure0NN
infrastructure0NN
service0.0197870712294258NN
arise0VB
city0NN
force0.0410992516792148NN
growth0.208565600657229NN
happen0VB

In many cases this growth involves low income residents who have little capacity to pay for infrastructure which leads to the spread of slums

Label: 10, with a score of: 0.502486653299386
WordScorePoS
growth0.11178243319132NN
involve0.0488696285210061VB
income0.166475342664294NN
capacity0NN
pay0.02794560829783NN
infrastructure0NN
lead0NN
spread0NN
slum0NN

Label: 9, with a score of: 0.492067633625032
WordScorePoS
growth0.0260040237236563NN
involve0VB
income0.110649350233433NN
capacity0NN
pay0NN
infrastructure0.192202909044225NN
lead0.0188543197850406NN
spread0NN
slum0NN

Label: 8, with a score of: 0.482372389468554
WordScorePoS
growth0.208565600657229NN
involve0VB
income0NN
capacity0.0123650647908625NN
pay0.0695218668857431NN
infrastructure0NN
lead0.0504071033101074NN
spread0NN
slum0NN

Label: 11, with a score of: 0.295153129493274
WordScorePoS
growth0NN
involve0VB
income0NN
capacity0.0112376090361851NN
pay0.0315914058171786NN
infrastructure0.0194583992186006NN
lead0NN
spread0NN
slum0.146277152173395NN

Rapid Urbanization in Africa Sprawl or Leapfrog

Label: 11, with a score of: 0.931736610454776
WordScorePoS
rapid0.0373517915079663JJ
urbanization0.146277152173395NN
africa0IN
leapfrog0VB

The dangers and opportunities of rapid urbanization are nowhere more apparent than in Africa the fastest urbanizing continent

Label: 11, with a score of: 0.847180618327512
WordScorePoS
opportunity0.0164178645080222NN
rapid0.0373517915079663JJ
urbanization0.146277152173395NN
africa0IN
continent0NN

African cities have been expanding with little coordination and their situation illustrates the need to manage the urban form through land policy transportation planning and service provision

Label: 11, with a score of: 0.869963388487618
WordScorePoS
african0IN
city0.607697021770652NN
expand0.0268848280079943VB
coordination0NN
situation0.0631828116343571NN
manage0VB
urban0.27622591898666JJ
form0NN
land0.0389167984372011NN
policy0.0136980135278128NN
transportation0.04477822029736NN
plan0.0583751976558017NN
service0.0269743071558856NN
provision0NN

Label: 16, with a score of: 0.183502278087164
WordScorePoS
african0IN
city0NN
expand0VB
coordination0NN
situation0NN
manage0VB
urban0JJ
form0.158049144643351NN
land0NN
policy0.0201317944152378NN
transportation0NN
plan0NN
service0NN
provision0.0658099748168827NN

Although only percent of Africans currently live in urban areas over the next two decades Africa urban population is projected to increase at an average annual rate of

Label: 11, with a score of: 0.758643650274471
WordScorePoS
african0IN
live0.0269743071558856VB
urban0.27622591898666JJ
decade0.055245183797332NN
africa0IN
population0.0194583992186006NN
project0NN
increase0.00323105002784081NN
average0NN
rate0NN

Label: 10, with a score of: 0.422842381945352
WordScorePoS
african0IN
live0.0159075630937118VB
urban0.0488696285210061JJ
decade0NN
africa0IN
population0.0860640399817412NN
project0NN
increase0.00285817158600878NN
average0.146608885563018NN
rate0.0172128079963482NN

Label: 13, with a score of: 0.390044822065952
WordScorePoS
african0IN
live0VB
urban0JJ
decade0.124232265906675NN
africa0IN
population0NN
project0.0335649154577099NN
increase0.0145316075945746NN
average0.1656430212089NN
rate0NN

percent compared to the world average annual growth rate of

Label: 10, with a score of: 0.630707610738529
WordScorePoS
compare0VB
world0.00276879546803864NN
average0.146608885563018NN
growth0.11178243319132NN
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Label: 13, with a score of: 0.477093966985192
WordScorePoS
compare0.0414107553022249VB
world0.00351929966767313NN
average0.1656430212089NN
growth0NN
rate0NN

Label: 8, with a score of: 0.476447217330787
WordScorePoS
compare0VB
world0.00172202218602071NN
average0NN
growth0.208565600657229NN
rate0NN

percent

Label: 0, with a score of: 1

The driving forces include push and pull factors of rural urban migration natural increase and reclassification of formerly rural areas as urban Kessides UN Habitat

Label: 11, with a score of: 0.796420701319065
WordScorePoS
drive0NN
force0NN
include0VB
rural0.0373517915079663JJ
urban0.27622591898666JJ
migration0NN
natural0.0229054731708393JJ
increase0.00323105002784081NN
habitat0NN

Label: 2, with a score of: 0.415260727373735
WordScorePoS
drive0NN
force0.0297950283197006NN
include0VB
rural0.148975141598503JJ
urban0JJ
migration0NN
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increase0.0128868286091919NN
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Label: 10, with a score of: 0.36094277916061
WordScorePoS
drive0NN
force0NN
include0VB
rural0.0330412182586772JJ
urban0.0488696285210061JJ
migration0.12939607756667NN
natural0JJ
increase0.00285817158600878NN
habitat0NN

By nearly million new urban dwellers will reside in African cities as indicated in Figure

Label: 11, with a score of: 0.994948015054248
WordScorePoS
urban0.27622591898666JJ
african0IN
city0.607697021770652NN
figure0NN

This will result in unprecedented needs for infrastructure and investment

Label: 9, with a score of: 0.790953591104975
WordScorePoS
result0NN
infrastructure0.192202909044225NN
investment0.0377086395700813NN

Label: 7, with a score of: 0.355971464023959
WordScorePoS
result0NN
infrastructure0.0651354046449052NN
investment0.0383371018038178NN

Label: 17, with a score of: 0.308393134411805
WordScorePoS
result0NN
infrastructure0.0130185595639838NN
investment0.0766240489430275NN

At the same time African cities account for over percent of the continent total GDP and the rapid growth of cities could allow countries to harness the benefits of urbanization fueling economic growth and sustainable development

Label: 11, with a score of: 0.85783706558167
WordScorePoS
time0NN
african0IN
city0.607697021770652NN
account0.0194583992186006NN
continent0NN
total0NN
gdp0NN
rapid0.0373517915079663JJ
growth0NN
country0NN
urbanization0.146277152173395NN
fuel0NN
economic0.0389167984372011JJ
sustainable0.00782503464617421JJ
development0.0100241617629471NN

Label: 8, with a score of: 0.324468570255347
WordScorePoS
time0NN
african0IN
city0NN
account0NN
continent0NN
total0NN
gdp0NN
rapid0JJ
growth0.208565600657229NN
country0NN
urbanization0NN
fuel0NN
economic0.128463821595706JJ
sustainable0.00688808874408283JJ
development0.0055149369084567NN

African countries are at various levels of urbanization and the urban transition will continue to African Urbanization Trend BUILDING SUSTAINABILITY IN AN URBANIZING WORLD proceed differently

Label: 11, with a score of: 0.915550820229417
WordScorePoS
african0IN
country0NN
level0.00692515656684925NN
urbanization0.146277152173395NN
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continue0.0537696560159885VB
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN

Label: 17, with a score of: 0.264172932275425
WordScorePoS
african0IN
country0NN
level0.0138997399903163NN
urbanization0NN
urban0JJ
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building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

There will be much slower urbanization in coming years in countries like South Africa which is already more than percent urbanized than in East African countries like Tanzania and Kenya less than percent urban with average annual urban growth greater than percent between and

Label: 11, with a score of: 0.841171938561145
WordScorePoS
urbanization0.146277152173395NN
country0NN
south0RB
africa0IN
african0IN
urban0.27622591898666JJ
average0NN
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Label: 10, with a score of: 0.428731383766704
WordScorePoS
urbanization0NN
country0NN
south0RB
africa0IN
african0IN
urban0.0488696285210061JJ
average0.146608885563018NN
growth0.11178243319132NN

Label: 8, with a score of: 0.251083800867019
WordScorePoS
urbanization0NN
country0NN
south0RB
africa0IN
african0IN
urban0JJ
average0NN
growth0.208565600657229NN

Label: 13, with a score of: 0.199411020902623
WordScorePoS
urbanization0NN
country0NN
south0RB
africa0IN
african0IN
urban0JJ
average0.1656430212089NN
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Urbanization inevitably results in transformations of urban form

Label: 11, with a score of: 0.87305516245832
WordScorePoS
urbanization0.146277152173395NN
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urban0.27622591898666JJ
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Label: 16, with a score of: 0.326590813349915
WordScorePoS
urbanization0NN
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urban0JJ
form0.158049144643351NN

Projections indicate that the built up area of cities in developing countries will triple between and while doubling their populations within the same period

Label: 11, with a score of: 0.936786298254248
WordScorePoS
projection0NN
build0VB
city0.607697021770652NN
develop0VB
country0NN
triple0RB
double0RB
population0.0194583992186006NN
period0NN

As cities built up areas grow their density usually declines as can be observed in Addis Ababa and Nairobi which are both among Africa most populous cities

Label: 11, with a score of: 0.990991998135023
WordScorePoS
city0.607697021770652NN
build0VB
grow0.0229054731708393VB
density0.0731385760866977NN
decline0.0315914058171786NN
africa0IN

During the last decade Nairobi and Addis Ababa experienced average annual density declines of about and

Label: 13, with a score of: 0.837328233511852
WordScorePoS
decade0.124232265906675NN
experienced0.0548233071065585JJ
average0.1656430212089NN
density0NN
decline0.0236803262479443NN

Label: 10, with a score of: 0.396763761101069
WordScorePoS
decade0NN
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average0.146608885563018NN
density0NN
decline0.02794560829783NN

Label: 11, with a score of: 0.363624831549609
WordScorePoS
decade0.055245183797332NN
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average0NN
density0.0731385760866977NN
decline0.0315914058171786NN

percent respectively while their built up areas increased by

Label: 1, with a score of: 0.534230703035819
WordScorePoS
build0.0614198493690006VB
increase0NN

Label: 4, with a score of: 0.487336355925453
WordScorePoS
build0.0416255041156371VB
increase0.0144029596227568NN

Label: 17, with a score of: 0.472333088022879
WordScorePoS
build0.0499801157438617VB
increase0.00432344066632653NN

Label: 9, with a score of: 0.406224789929061
WordScorePoS
build0.030745604615229VB
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percent per annum respectively

Label: 8, with a score of: 1
WordScorePoS
annum0.0804764811724088NN

Johannesburg in contrast displayed different pattern its built up area increased by only

Label: 12, with a score of: 0.585760138245211
WordScorePoS
pattern0.0819583881728876NN
build0VB
increase0.00958677785775002NN

percent and urban density rose by

Label: 11, with a score of: 0.93180515788709
WordScorePoS
urban0.27622591898666JJ
density0.0731385760866977NN
rise0.0268848280079943NN

percent per annum between and

Label: 8, with a score of: 1
WordScorePoS
annum0.0804764811724088NN

If similar density changes are experienced in the next years the cities forms would be drastically altered as shown in Figure

Label: 11, with a score of: 0.940074647094498
WordScorePoS
density0.0731385760866977NN
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city0.607697021770652NN
form0NN
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Label: 16, with a score of: 0.218228885713047
WordScorePoS
density0NN
experienced0JJ
city0NN
form0.158049144643351NN
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Although sustained increases in sprawl over two decade period are unlikely in the face of other regulating factors even an annual density decline of percent would increase African urban cover from

Label: 11, with a score of: 0.804293120857172
WordScorePoS
sustained0JJ
increase0.00323105002784081NN
decade0.055245183797332NN
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regulate0VB
density0.0731385760866977NN
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african0IN
urban0.27622591898666JJ
cover0NN

Label: 13, with a score of: 0.321554692846661
WordScorePoS
sustained0JJ
increase0.0145316075945746NN
decade0.124232265906675NN
period0NN
regulate0VB
density0NN
decline0.0236803262479443NN
african0IN
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cover0NN

percent of total arable land in to about

Label: 15, with a score of: 0.770677380769212
WordScorePoS
total0NN
arable0.0517339898971162JJ
land0.123873694868802NN

Label: 7, with a score of: 0.374975759847372
WordScorePoS
total0.0528748274225028NN
arable0JJ
land0.0325677023224526NN

Label: 2, with a score of: 0.314950850991458
WordScorePoS
total0.0252000451111224NN
arable0JJ
land0.0465651203624806NN

percent by

Label: 0, with a score of: 1

This has implications for food security and water management issues both of which are already concern for the continent

Label: 6, with a score of: 0.715024321442845
WordScorePoS
food0.0214003646985842NN
security0.0348974218717993NN
water0.556409482163189NN
management0.0428007293971684NN
issue0NN
continent0NN

Label: 2, with a score of: 0.464455485461432
WordScorePoS
food0.292342270824999NN
security0.0893850849591018NN
water0NN
management0NN
issue0NN
continent0.0440683498519077NN

Label: 12, with a score of: 0.444797481110493
WordScorePoS
food0.186896403054502NN
security0NN
water0.186896403054502NN
management0.0339811641917276NN
issue0NN
continent0NN

Provision of urban infrastructure and services has not kept pace with urbanization in most African cities

Label: 11, with a score of: 0.963595175246148
WordScorePoS
provision0NN
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city0.607697021770652NN

Label: 9, with a score of: 0.185271401787438
WordScorePoS
provision0NN
urban0JJ
infrastructure0.192202909044225NN
service0.0148023490369527NN
pace0NN
urbanization0NN
african0IN
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As their populations increase and people become more affluent the cities are faced with daunting challenges in managing transportation water and wastewater solid waste and energy with demand far outstripping supply

Label: 11, with a score of: 0.557020985184552
WordScorePoS
population0.0194583992186006NN
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transportation0.04477822029736NN
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Label: 6, with a score of: 0.549035927259571
WordScorePoS
population0.0363595928905615NN
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city0NN
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challenge0NN
manage0VB
transportation0NN
water0.556409482163189NN
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waste0NN
energy0.0153390539205676NN
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Label: 7, with a score of: 0.418395938775963
WordScorePoS
population0NN
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city0NN
face0NN
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manage0VB
transportation0NN
water0NN
wastewater0NN
waste0.0625160381065121NN
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Label: 12, with a score of: 0.36320212197181
WordScorePoS
population0.0144336476662975NN
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city0NN
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In addition uncoordinated growth of cities over the past few years has dispersed their populations with more people living in the urban peripheries and thereby increasing the cost of infrastructure and service provision

Label: 11, with a score of: 0.816683243786568
WordScorePoS
addition0NN
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city0.607697021770652NN
population0.0194583992186006NN
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Label: 9, with a score of: 0.255655527292436
WordScorePoS
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Label: 8, with a score of: 0.244997735899129
WordScorePoS
addition0NN
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service0.0197870712294258NN
provision0NN

Equal access to infrastructure and basic services is an issue requiring urgent intervention in African cities

Label: 11, with a score of: 0.812411826126605
WordScorePoS
equal0JJ
access0.00626002771693937NN
infrastructure0.0194583992186006NN
basic0.0583751976558017JJ
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african0IN
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Label: 9, with a score of: 0.332239452334352
WordScorePoS
equal0JJ
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basic0.0480507272610562JJ
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On the other hand African cities have unique opportunity to harness the benefits of urbanization

Label: 11, with a score of: 0.990796242315168
WordScorePoS
african0IN
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opportunity0.0164178645080222NN
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As discussed in Chapter in cities where rapid growth is occurring and many investment decisions on infrastructure and land use development are currently taking place making the right decisions will influence the form that the cities adopt

Label: 11, with a score of: 0.852592464763236
WordScorePoS
city0.607697021770652NN
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Label: 9, with a score of: 0.202778790415532
WordScorePoS
city0NN
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Label: 8, with a score of: 0.189319386919719
WordScorePoS
city0NN
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Label: 16, with a score of: 0.182525975076852
WordScorePoS
city0NN
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Going forward urban planning should promote settlement patterns that capitalize on agglomeration economies derived from lower per head costs of infrastructure networks high reliance on public and non motorized transport and more efficiently planned cities Glaeser and Kenworthy

Label: 11, with a score of: 0.921872080637611
WordScorePoS
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Label: 9, with a score of: 0.175378460828626
WordScorePoS
urban0JJ
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At present public transport represents only small fraction of the amount invested in road infrastructure and maintenance in most African cities UITP

Label: 11, with a score of: 0.906983119404626
WordScorePoS
public0.0583751976558017NN
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Label: 9, with a score of: 0.310762535472136
WordScorePoS
public0.0160169090870187NN
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By promoting low carbon public transport African cities could leapfrog past unsustainable transport development stages experienced by other cities

Label: 11, with a score of: 0.966064591823602
WordScorePoS
promote0VB
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The time is now for African cities to act if they are to move toward sustainable urban development

Label: 11, with a score of: 0.971572889093371
WordScorePoS
time0NN
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sustainable0.00782503464617421JJ
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This should be viewed as win win situation where the cities achieve their development agendas while reaping environmental and social co benefits

Label: 11, with a score of: 0.87717429136212
WordScorePoS
view0NN
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PART II THE PATH TO SUSTAINABILITY Addis Abba Ethiopia FIG

Label: 0, with a score of: 1

Spatial Growth of Three African Cities Nairobi Kenya Johannesburg South Africa Source Maps created by Henry Jewell World Bank Urban Development and Resilience Unit and Katie McWilliams and Alex Stoicof World Bank Sustainable Development Network Information Solutions

Label: 11, with a score of: 0.8098631824413
WordScorePoS
growth0NN
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Label: 8, with a score of: 0.244158844450353
WordScorePoS
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Energy Cities are the major global energy users IEA and the major actors to improve society energy efficiency while simultaneously decreasing the associated carbon emissions

Label: 7, with a score of: 0.784326255716526
WordScorePoS
building0NN
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Label: 11, with a score of: 0.462334261271769
WordScorePoS
building0.0747035830159326NN
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Label: 12, with a score of: 0.263518915162575
WordScorePoS
building0NN
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Label: 13, with a score of: 0.181265683009632
WordScorePoS
building0NN
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Label: 17, with a score of: 0.157980900940192
WordScorePoS
building0.149940347231585NN
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Many cities especially in the OECD have already taken action in this area with policies that increase energy efficiency and promote cleaner energy sources

Label: 7, with a score of: 0.776529284305476
WordScorePoS
city0NN
oecd0NN
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Label: 11, with a score of: 0.495271879567491
WordScorePoS
city0.607697021770652NN
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Label: 12, with a score of: 0.296823261857763
WordScorePoS
city0NN
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Worldwide of the cities that participated in voluntary reporting through the Carbon Disclosure Project in almost half percent have renewable energy target CDP

Label: 7, with a score of: 0.675687149296191
WordScorePoS
worldwide0RB
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Label: 11, with a score of: 0.655433010902283
WordScorePoS
worldwide0RB
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Label: 12, with a score of: 0.249979342498306
WordScorePoS
worldwide0RB
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report0.0409791940864438NN
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Local strategies can focus on improving energy conservation increasing the use of renewable energy improving the efficiency of fossil based power generation facilities transitioning to less carbon intensive fuels for example from coal to natural gas and employing carbon capture and storage technology

Label: 7, with a score of: 0.858091813045081
WordScorePoS
local0JJ
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Label: 12, with a score of: 0.272962764906997
WordScorePoS
local0.0144336476662975JJ
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The use of these strategies depends on the institutional capacity of the city the local resource base and the willingness of the constituents to bear the price impacts of these policies OECD

Label: 11, with a score of: 0.746461222626575
WordScorePoS
strategy0NN
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Label: 12, with a score of: 0.312354900701716
WordScorePoS
strategy0NN
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The OECD report Towards Green Growth OECD describes some of the systems cities are developing to produce renewable power locally

Label: 11, with a score of: 0.792541301757276
WordScorePoS
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Label: 8, with a score of: 0.241638520395181
WordScorePoS
oecd0NN
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Label: 7, with a score of: 0.183778077845954
WordScorePoS
oecd0NN
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Label: 12, with a score of: 0.403010674440503
WordScorePoS
oecd0.108503934593034NN
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Some cities have invested heavily in clean heat production in wind turbines that are typically placed outside city boundaries and in photovoltaic systems located on buildings or in dedicated open areas

Label: 11, with a score of: 0.944525289470694
WordScorePoS
city0.607697021770652NN
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Label: 17, with a score of: 0.122257220361933
WordScorePoS
city0NN
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Label: 7, with a score of: 0.116749338442898
WordScorePoS
city0NN
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Among the innovators ' Cities such as Toronto and Amsterdam use lakewater air conditioning and seawater heating

Label: 11, with a score of: 0.975493681264172
WordScorePoS
city0.607697021770652NN
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' Copenhagen district heating system which captures waste heat from power generation that is normally released into the sea as hot water has helped reduce emissions and has taken off the average household bill each year

Label: 6, with a score of: 0.565691134178164
WordScorePoS
heating0NN
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Label: 12, with a score of: 0.545546663306043
WordScorePoS
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Label: 13, with a score of: 0.457556818759908
WordScorePoS
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Label: 10, with a score of: 0.180792446679199
WordScorePoS
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reduce0.0310356414389692VB
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Copenhagen also produces energy from much of its waste sending mere percent to landfills

Label: 7, with a score of: 0.854259087545537
WordScorePoS
produce0VB
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Label: 12, with a score of: 0.46874610497219
WordScorePoS
produce0.0332151194779746VB
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' In Freiburg Germany photovoltaic panels cover square meters square feet of the city building surfaces including the main railway station

Label: 11, with a score of: 0.944977566700169
WordScorePoS
panel0NN
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Label: 17, with a score of: 0.207635021837934
WordScorePoS
panel0NN
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' San Francisco operates the largest city owned solar power system in the United States

Label: 11, with a score of: 0.92600601680899
WordScorePoS
operate0VB
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' The London Array offshore wind turbine system is due to produce megawatts or enough to power homes

Label: 1, with a score of: 0.66626712863019
WordScorePoS
array0NN
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home0.0908430554521382NN

' In Venice opened the first megawatt hydrogen fuelled power station serving households

Label: 5, with a score of: 0.570566290639869
WordScorePoS
fuel0.0558185411034276NN
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Cities that are not energy producers on the other hand can adopt regulations that promote connection with renewable energy sources and the supply of clean energy to the city grid

Label: 7, with a score of: 0.763520991075068
WordScorePoS
city0NN
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Label: 11, with a score of: 0.570120360034031
WordScorePoS
city0.607697021770652NN
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Label: 12, with a score of: 0.233820681018974
WordScorePoS
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Cities are also testing alternative models to the traditional central station grid model

Label: 11, with a score of: 0.960283434138502
WordScorePoS
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alternative0NN
traditional0JJ
central0JJ

There are experiments with on site photovoltaics and wind power and with smart grid technologies that monitor the electricity consumed by each household and provide data to inform energy management see the Smart Cities section below

Label: 11, with a score of: 0.641200708455995
WordScorePoS
power0NN
technology0NN
monitor0NN
electricity0NN
consume0VB
household0NN
provide0.0269743071558856VB
datum0NN
energy0.0492535935240667NN
management0.0687164195125178NN
city0.607697021770652NN

Label: 7, with a score of: 0.56828094321955
WordScorePoS
power0NN
technology0.109914925284606NN
monitor0NN
electricity0.0625160381065121NN
consume0VB
household0NN
provide0VB
datum0NN
energy0.494617163780726NN
management0NN
city0NN

Label: 12, with a score of: 0.270538463003153
WordScorePoS
power0NN
technology0NN
monitor0.0332151194779746NN
electricity0NN
consume0.0409791940864438VB
household0.0554128417527413NN
provide0.0200087191734851VB
datum0NN
energy0.133960988253756NN
management0.0339811641917276NN
city0NN

A combination of energy management systems small scale distributed energy sources such as solar panels on buildings or mini wind turbines in the city and energy storage resources could help to integrate all energy consumption sectors Figure and optimize the city energy efficiency

Label: 7, with a score of: 0.737084841050531
WordScorePoS
energy0.494617163780726NN
management0NN
system0NN
scale0NN
distribute0VB
source0.0274787313211514NN
panel0NN
building0NN
city0NN
resource0NN
integrate0VB
consumption0NN
sector0NN
figure0NN

Label: 11, with a score of: 0.537935701507015
WordScorePoS
energy0.0492535935240667NN
management0.0687164195125178NN
system0.0315914058171786NN
scale0NN
distribute0VB
source0NN
panel0NN
building0.0747035830159326NN
city0.607697021770652NN
resource0.0449504361447405NN
integrate0.0537696560159885VB
consumption0.08955644059472NN
sector0NN
figure0NN

Label: 12, with a score of: 0.32853262328688
WordScorePoS
energy0.133960988253756NN
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system0NN
scale0NN
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source0NN
panel0NN
building0NN
city0NN
resource0.0416785799329393NN
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consumption0.332151194779746NN
sector0.0169905820958638NN
figure0NN

Label: 17, with a score of: 0.0944874047881561
WordScorePoS
energy0.0109843026967392NN
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system0.0211360962287062NN
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source0.0109843026967392NN
panel0NN
building0.149940347231585NN
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resource0.0375923735942499NN
integrate0VB
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sector0.0459744293658165NN
figure0NN

Label: 8, with a score of: 0.0633640969069491
WordScorePoS
energy0NN
management0NN
system0NN
scale0NN
distribute0VB
source0NN
panel0NN
building0NN
city0NN
resource0.0123650647908625NN
integrate0.0295821503613099VB
consumption0.147812295323721NN
sector0.0252035516550537NN
figure0NN

Ideally cities will be able to develop partial energy self reliance by means of distributed modes of energy generation using wind turbines geothermal energy solar power biomass and other resources

Label: 7, with a score of: 0.816826161724459
WordScorePoS
city0NN
develop0VB
energy0.494617163780726NN
means0NNS
distribute0VB
generation0NN
power0NN
resource0NN

Label: 11, with a score of: 0.440606349807341
WordScorePoS
city0.607697021770652NN
develop0VB
energy0.0492535935240667NN
means0NNS
distribute0VB
generation0NN
power0NN
resource0.0449504361447405NN

Label: 12, with a score of: 0.289286651782956
WordScorePoS
city0NN
develop0VB
energy0.133960988253756NN
means0NNS
distribute0VB
generation0.0819583881728876NN
power0NN
resource0.0416785799329393NN

Until renewables become more commercially competitive however saving energy through PART II THE PATH TO SUSTAINABILITY FIG

Label: 7, with a score of: 0.937358355248979
WordScorePoS
renewable0.158624482267508JJ
save0IN
energy0.494617163780726NN
sustainability0NN

Label: 12, with a score of: 0.284653113227364
WordScorePoS
renewable0.0234335422829798JJ
save0IN
energy0.133960988253756NN
sustainability0.0409791940864438NN

Energy Consumption Sectors Across Sample of Cities Source Adapted from Kennedy Marine Aviation Transportation Electricity Denver Chicago Los Angeles Toronto GTA New York City Geneva London Bangkok Shanghai Paris IDF Prague Beijing Tianjin Barcelona Cape Town Jakarta Amman Dar es Salaam Energy Consumption GJ cap

Label: 11, with a score of: 0.685381977798502
WordScorePoS
energy0.0492535935240667NN
consumption0.08955644059472NN
sector0NN
city0.607697021770652NN
source0NN
marine0JJ
transportation0.04477822029736NN
electricity0NN
paris0NN
beij0NN

Label: 7, with a score of: 0.481003941535729
WordScorePoS
energy0.494617163780726NN
consumption0NN
sector0NN
city0NN
source0.0274787313211514NN
marine0JJ
transportation0NN
electricity0.0625160381065121NN
paris0NN
beij0NN

Label: 12, with a score of: 0.452665091748236
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN
sector0.0169905820958638NN
city0NN
source0NN
marine0.0332151194779746JJ
transportation0.0332151194779746NN
electricity0NN
paris0NN
beij0NN

Label: 14, with a score of: 0.213871595684002
WordScorePoS
energy0NN
consumption0NN
sector0NN
city0NN
source0.0238835024181023NN
marine0.423410411231031JJ
transportation0.0325700316331562NN
electricity0NN
paris0NN
beij0NN

Label: 8, with a score of: 0.142990585996629
WordScorePoS
energy0NN
consumption0.147812295323721NN
sector0.0252035516550537NN
city0NN
source0NN
marine0JJ
transportation0NN
electricity0NN
paris0NN
beij0NN

Heating Industrial Photo Francis Dobbs World Bank BUILDING SUSTAINABILITY IN AN URBANIZING WORLD

Label: 9, with a score of: 0.698579127975205
WordScorePoS
heating0NN
industrial0.318320101004075JJ
world0.00257642711761688NN
bank0NN
building0NN
sustainability0NN

Label: 17, with a score of: 0.501550373446941
WordScorePoS
heating0NN
industrial0JJ
world0.00418824502414673NN
bank0.036961556179878NN
building0.149940347231585NN
sustainability0.036961556179878NN

FIG

Label: 0, with a score of: 1

Costs of Renewable Energy Fuel Cost Cost Capital Cost

Label: 7, with a score of: 0.824332962808673
WordScorePoS
cost0NN
renewable0.158624482267508JJ
energy0.494617163780726NN
fuel0.0749457204978913NN

Label: 12, with a score of: 0.341235090021548
WordScorePoS
cost0.0277064208763707NN
renewable0.0234335422829798JJ
energy0.133960988253756NN
fuel0.0332151194779746NN

Leveraged Cost USD kwh

Label: 10, with a score of: 0.699218736404889
WordScorePoS
cost0.0660824365173543NN

Label: 16, with a score of: 0.580849129119786
WordScorePoS
cost0.0548954478802642NN

Central PV Commercial PV Residential PV Central CSP Central Wind Central Wind Offshore NGCC Source Adapted from OECD

Label: 12, with a score of: 0.436743059285741
WordScorePoS
central0JJ
commercial0.0542519672965169JJ
residential0.0542519672965169JJ
source0NN
oecd0.108503934593034NN

efficiency measures remains the most cost effective short term strategy

Label: 17, with a score of: 0.619257588788036
WordScorePoS
measure0NN
remains0.036961556179878NNS
cost0NN
term0.149793392961411NN
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Label: 1, with a score of: 0.44795069357168
WordScorePoS
measure0.103895385378356NN
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strategy0.0442083771593746NN

Label: 13, with a score of: 0.360503573553637
WordScorePoS
measure0.0710409787438328NN
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cost0.0279982034978914NN
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strategy0.0201523636533764NN

Label: 10, with a score of: 0.319676348057128
WordScorePoS
measure0NN
remains0NNS
cost0.0660824365173543NN
term0.0396106020715138NN
strategy0NN

While the renewable energy industry has reached considerable size in developed countries in there were approximately million renewable energy jobs worldwide and costs have dropped substantially most renewable energy sources are yet not price competitive with conventional sources UNEP and New Energy Finance

Label: 7, with a score of: 0.914652733448952
WordScorePoS
renewable0.158624482267508JJ
energy0.494617163780726NN
industry0NN
reach0NN
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develop0VB
country0NN
approximately0RB
job0.0229264916384433NN
worldwide0RB
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substantially0RB
source0.0274787313211514NN
price0NN
finance0NN

Label: 9, with a score of: 0.255818741532225
WordScorePoS
renewable0.0520080474473126JJ
energy0.0540565419125769NN
industry0.181897200573757NN
reach0NN
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develop0VB
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approximately0RB
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worldwide0RB
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substantially0RB
source0.0135141354781442NN
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Label: 12, with a score of: 0.24828786479031
WordScorePoS
renewable0.0234335422829798JJ
energy0.133960988253756NN
industry0NN
reach0.0332151194779746NN
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country0NN
approximately0RB
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worldwide0RB
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substantially0RB
source0NN
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Label: 8, with a score of: 0.0667946201032015
WordScorePoS
renewable0JJ
energy0NN
industry0NN
reach0NN
size0.049270765107907NN
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approximately0RB
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substantially0RB
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finance0NN

In addition distributed generation is more expensive than centralized generation and residential solar photovoltaics are more expensive than commercial solar photovoltaics Figure

Label: 12, with a score of: 0.795344298865879
WordScorePoS
addition0NN
distribute0VB
generation0.0819583881728876NN
expensive0.0542519672965169JJ
residential0.0542519672965169JJ
commercial0.0542519672965169JJ
figure0NN

Label: 9, with a score of: 0.582446589988142
WordScorePoS
addition0.0368585655748969NN
distribute0VB
generation0.0909486002868787NN
expensive0JJ
residential0JJ
commercial0JJ
figure0.0602029956716497NN

Empirical evidence from California suggests that at

Label: 10, with a score of: 0.906670803599693
WordScorePoS
evidence0.12939607756667NN
suggest0VB

Label: 9, with a score of: 0.421838895669765
WordScorePoS
evidence0NN
suggest0.0602029956716497VB

per kilowatt hour the average cost of energy efficiency is significantly lower than the cost of renewable energy OECD

Label: 7, with a score of: 0.87316558074633
WordScorePoS
average0NN
cost0NN
energy0.494617163780726NN
renewable0.158624482267508JJ
oecd0NN

Label: 12, with a score of: 0.346321424356718
WordScorePoS
average0NN
cost0.0277064208763707NN
energy0.133960988253756NN
renewable0.0234335422829798JJ
oecd0.108503934593034NN

Label: 10, with a score of: 0.212060619946013
WordScorePoS
average0.146608885563018NN
cost0.0660824365173543NN
energy0NN
renewable0JJ
oecd0NN

Label: 13, with a score of: 0.205335386080594
WordScorePoS
average0.1656430212089NN
cost0.0279982034978914NN
energy0.0123065238088614NN
renewable0.0236803262479443JJ
oecd0NN

Nevertheless investments in renewable energy are needed today to reap benefits over the medium to long term and cities have role to play here as well

Label: 7, with a score of: 0.712430878110159
WordScorePoS
investment0.0383371018038178NN
renewable0.158624482267508JJ
energy0.494617163780726NN
medium0NN
term0.0749457204978913NN
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Label: 11, with a score of: 0.610589645933852
WordScorePoS
investment0NN
renewable0JJ
energy0.0492535935240667NN
medium0NN
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city0.607697021770652NN

Label: 17, with a score of: 0.220648316335136
WordScorePoS
investment0.0766240489430275NN
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energy0.0109843026967392NN
medium0NN
term0.149793392961411NN
city0NN

Label: 12, with a score of: 0.146287207036463
WordScorePoS
investment0NN
renewable0.0234335422829798JJ
energy0.133960988253756NN
medium0NN
term0NN
city0NN

Feed in tariffs can be particularly useful to promote supply

Label: 12, with a score of: 0.610201200059514
WordScorePoS
promote0.0148712355431627VB
supply0.0703006268489394NN

Label: 7, with a score of: 0.438913946325687
WordScorePoS
promote0.00838876601975825VB
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Label: 6, with a score of: 0.422919557005958
WordScorePoS
promote0VB
supply0.0590310971343993NN

Label: 4, with a score of: 0.332262099733743
WordScorePoS
promote0.0111711050494158VB
supply0.0352060273355634NN

With this system producers of renewable energy contribute solar electricity into the public grid and receive premium tariff per generated kilowatt hour which reflects the benefits of renewable electricity compared to electricity generated from fossil fuels

Label: 7, with a score of: 0.861088994365969
WordScorePoS
system0NN
producer0NN
renewable0.158624482267508JJ
energy0.494617163780726NN
contribute0VB
electricity0.0625160381065121NN
public0NN
receive0VB
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compare0VB
fossil0.0924643738905717NN
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Label: 12, with a score of: 0.280638988539817
WordScorePoS
system0NN
producer0.0409791940864438NN
renewable0.0234335422829798JJ
energy0.133960988253756NN
contribute0.0554128417527413VB
electricity0NN
public0.028867295332595NN
receive0VB
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fossil0.0409791940864438NN
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Feed in tariffs have helped attract investment for renewable energy in Europe and in number of United States cities such as Gainesville Florida and Los Angeles OECD

Label: 7, with a score of: 0.663157151427813
WordScorePoS
help0NN
attract0VB
investment0.0383371018038178NN
renewable0.158624482267508JJ
energy0.494617163780726NN
unite0VB
city0NN
oecd0NN

Label: 11, with a score of: 0.629952120450056
WordScorePoS
help0NN
attract0VB
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renewable0JJ
energy0.0492535935240667NN
unite0VB
city0.607697021770652NN
oecd0NN

Label: 12, with a score of: 0.306993309808325
WordScorePoS
help0.0542519672965169NN
attract0VB
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renewable0.0234335422829798JJ
energy0.133960988253756NN
unite0VB
city0NN
oecd0.108503934593034NN

Other strategies that can be highly attractive for renewable energy project developers include direct purchasing of renewable electricity and renewable equipment soft loans and guarantees provided by city or regional governments

Label: 7, with a score of: 0.757446472274825
WordScorePoS
strategy0NN
highly0RB
renewable0.158624482267508JJ
energy0.494617163780726NN
project0NN
include0VB
direct0JJ
electricity0.0625160381065121NN
guarantee0NN
city0NN
regional0JJ
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Label: 11, with a score of: 0.537703054097167
WordScorePoS
strategy0NN
highly0RB
renewable0JJ
energy0.0492535935240667NN
project0NN
include0VB
direct0.04477822029736JJ
electricity0NN
guarantee0NN
city0.607697021770652NN
regional0.0315914058171786JJ
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Label: 12, with a score of: 0.198483320693349
WordScorePoS
strategy0NN
highly0RB
renewable0.0234335422829798JJ
energy0.133960988253756NN
project0.0664302389559493NN
include0VB
direct0JJ
electricity0NN
guarantee0NN
city0NN
regional0JJ
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Smart Cities The phrase smart city has been applied to everything from distributed power generation to high tech traffic management but it is particularly associated with programs that help cities improve their resource efficiency using information and communications technology ICT and PART II THE PATH TO SUSTAINABILITY technological infrastructure

Label: 11, with a score of: 0.905961734488421
WordScorePoS
city0.607697021770652NN
distribute0VB
power0NN
generation0NN
management0.0687164195125178NN
improve0.0273960270556257VB
resource0.0449504361447405NN
information0NN
communication0NN
technology0NN
sustainability0NN
technological0.0315914058171786JJ
infrastructure0.0194583992186006NN

Label: 9, with a score of: 0.311113284218787
WordScorePoS
city0NN
distribute0VB
power0.0454743001434393NN
generation0.0909486002868787NN
management0NN
improve0VB
resource0.0185001613201543NN
information0.0675706773907211NN
communication0.0922368138456871NN
technology0.0810848128688654NN
sustainability0NN
technological0.104016094894625JJ
infrastructure0.192202909044225NN

A broadband digital infrastructure can connect people to each other people to city systems and city systems to other city systems

Label: 11, with a score of: 0.960015741738576
WordScorePoS
digital0JJ
infrastructure0.0194583992186006NN
people0.0161552501392041NNS
city0.607697021770652NN
system0.0315914058171786NN

Label: 9, with a score of: 0.111829569547774
WordScorePoS
digital0JJ
infrastructure0.192202909044225NN
people0.0186171553883251NNS
city0NN
system0NN

Smart grids and similar technologies track and respond to data from energy transportation and ICT infrastructures allowing integrated management of these sectors

Label: 7, with a score of: 0.711404752572175
WordScorePoS
technology0.109914925284606NN
datum0NN
energy0.494617163780726NN
transportation0NN
infrastructure0.0651354046449052NN
integrate0VB
management0NN
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Label: 9, with a score of: 0.4561428757235
WordScorePoS
technology0.0810848128688654NN
datum0.0454743001434393NN
energy0.0540565419125769NN
transportation0NN
infrastructure0.192202909044225NN
integrate0VB
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Label: 12, with a score of: 0.283595714145385
WordScorePoS
technology0NN
datum0NN
energy0.133960988253756NN
transportation0.0332151194779746NN
infrastructure0.028867295332595NN
integrate0.0199423462679015VB
management0.0339811641917276NN
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Studies have shown that for city of inhabitants within years smart city infrastructure could bring significant savings and consumption of fuel and heat would decline by half as would carbon dioxide emissions

Label: 11, with a score of: 0.908198228466068
WordScorePoS
study0NN
city0.607697021770652NN
infrastructure0.0194583992186006NN
bring0.0731385760866977VB
consumption0.08955644059472NN
fuel0NN
decline0.0315914058171786NN
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN

Label: 12, with a score of: 0.285701745303774
WordScorePoS
study0NN
city0NN
infrastructure0.028867295332595NN
bring0VB
consumption0.332151194779746NN
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decline0.0234335422829798NN
carbon0NN
dioxide0NN
emission0.0554128417527413NN

Label: 13, with a score of: 0.191487395927935
WordScorePoS
study0NN
city0NN
infrastructure0NN
bring0VB
consumption0NN
fuel0NN
decline0.0236803262479443NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN

Label: 9, with a score of: 0.116074886501764
WordScorePoS
study0NN
city0NN
infrastructure0.192202909044225NN
bring0VB
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fuel0NN
decline0NN
carbon0NN
dioxide0NN
emission0NN

Label: 8, with a score of: 0.0892665750408508
WordScorePoS
study0NN
city0NN
infrastructure0NN
bring0VB
consumption0.147812295323721NN
fuel0NN
decline0NN
carbon0NN
dioxide0NN
emission0NN

Electricity consumption would fall by percent

Label: 12, with a score of: 0.813098687803777
WordScorePoS
electricity0NN
consumption0.332151194779746NN
fall0NN

Label: 8, with a score of: 0.361841189367627
WordScorePoS
electricity0NN
consumption0.147812295323721NN
fall0NN

Label: 3, with a score of: 0.30133212148907
WordScorePoS
electricity0NN
consumption0NN
fall0.123094312756122NN

One of the specific goals of smart grid ICT platforms is real time monitoring control and optimization of distributed energy resources

Label: 7, with a score of: 0.76579070776701
WordScorePoS
goal0NN
platform0NN
time0NN
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control0NN
distribute0VB
energy0.494617163780726NN
resource0NN

Label: 12, with a score of: 0.349664794593386
WordScorePoS
goal0NN
platform0NN
time0.0169905820958638NN
monitor0.0332151194779746NN
control0NN
distribute0VB
energy0.133960988253756NN
resource0.0416785799329393NN

Increasingly smart grids and their household counterparts smart meters and smart homes Box will allow energy systems to be managed in real time while providing more information to end users thus changing behavior and reducing energy demand

Label: 7, with a score of: 0.787996656319112
WordScorePoS
household0NN
home0NN
energy0.494617163780726NN
system0NN
manage0VB
time0NN
provide0VB
information0NN
user0NN
change0.105749654845006NN
reduce0.00838876601975825VB
demand0NN

Label: 13, with a score of: 0.307761613245799
WordScorePoS
household0NN
home0NN
energy0.0123065238088614NN
system0NN
manage0VB
time0NN
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information0NN
user0NN
change0.402565546215053NN
reduce0.00375696197697663VB
demand0NN

Label: 12, with a score of: 0.299363129717773
WordScorePoS
household0.0554128417527413NN
home0NN
energy0.133960988253756NN
system0NN
manage0VB
time0.0169905820958638NN
provide0.0200087191734851VB
information0.0365348149782971NN
user0NN
change0NN
reduce0.022306853314744VB
demand0NN

Not only households but operators of large buildings train stations power plants and other infrastructure will be able to directly manage their energy consumption and carbon emissions

Label: 7, with a score of: 0.608126911071613
WordScorePoS
household0NN
building0NN
power0NN
plant0NN
infrastructure0.0651354046449052NN
manage0VB
energy0.494617163780726NN
consumption0NN
carbon0.0625160381065121NN
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Label: 12, with a score of: 0.537988730741184
WordScorePoS
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WordScorePoS
household0NN
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WordScorePoS
household0NN
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Label: 2, with a score of: 0.195739133862003
WordScorePoS
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Label: 17, with a score of: 0.187295180583652
WordScorePoS
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Label: 8, with a score of: 0.131265552283304
WordScorePoS
household0NN
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Eco districts or clusters of buildings with networked infrastructure coordinated http suslab

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Label: 9, with a score of: 0.584130714193112
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Label: 16, with a score of: 0.333669426084193
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eu partners innovationcity ruhr through ICT platforms can be used to further increase energy efficiency

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Label: 12, with a score of: 0.261495554032417
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When businesses and residents produce part of their own electricity with small scale solar panels and small wind turbines smart technologies allow them to feed their excess production back into the grid helping the city to reach partial energy self reliance

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These energy positive buildings provide not only distributed energy resources but also flexible demand and storage points for example electric cars and stand alone batteries

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Storage technologies can be integrated to ensure continuous energy supply at the city scale and the smart grid can be designed to optimize energy balances based on real time load forecasts weather based generation forecasts and energy price forecasts

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These technologies can typically be used within the perimeter of the city as well as by regional authorities and regulatory bodies wishing to audit and reduce carbon emissions

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Integrated technologies will help dense cities work efficiently Zenghelis

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Smart connected cities will monitor and measure resource flows predict future behavior and simulate changes in demand as result of policy actions all of which will feed infrastructure investment decisions Hoornweg et al

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The applications go well beyond the energy grid

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Label: 12, with a score of: 0.258405227354763
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Smart transport systems Photo Julianne Baker Gallegos World Bank BUILDING SUSTAINABILITY IN AN URBANIZING WORLD in Singapore for example are used to tackle congestion establish road user charges and supply real time information on traffic problems

Label: 17, with a score of: 0.604177705089442
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Label: 11, with a score of: 0.472531273160216
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The spread of social media discussed further in Chapter could add another dimension to smart cities providing new mechanisms for society wide planning and collective action

Label: 11, with a score of: 0.781484482162386
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For instance as citizens and cities become more interconnected governments can begin to replace blunt regulations with highly personalized incentives and instructions

Label: 11, with a score of: 0.963977707747561
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These could be tailored in real time to coordinate the actions of individuals toward goals like peak load management in the energy Examples of the use of connected information technologies to improve the effectiveness resilience and efficiency of cities can be found at http www

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connectedurbandevelopment

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BOX sector

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Label: 17, with a score of: 0.453223591930217
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Experiments by Cisco in Amsterdam IBM in Dubuque Iowa see Box and others have shown that simply making citizens aware of their individual energy efficiency relative to that of their neighbors can encourage virtuous race to the top

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Label: 12, with a score of: 0.315230940969189
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The signs are encouraging smart city initiatives are underway in many urban centers

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Cities are already beginning to link solutions to policy goals and initiatives assessing the value of both householdlevel smart meters and city level smart grids

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For example San Diego benefits from planned smart grid implementation were estimated to be

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billion over years with an internal rate of return up to percent and payback period of

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Table shows ongoing technology enabled initiatives in cities of the network

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Case Study Smart Homes in Stratford Ontario In Stratford Ontario the smart home that controls domestic appliances from single interface in this case tablet is becoming reality

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home controls over the Internet

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Label: 5, with a score of: 0.385291932132885
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Label: 9, with a score of: 0.376298814002197
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For the technology partners this provides site living lab to develop and refine the systems

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Label: 9, with a score of: 0.306048154785991
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Stratford city of has begun series of pilot projects that leverage its municipal Wi Fi network

Label: 11, with a score of: 0.990188280663118
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One of the most recent initiatives is joint venture between the city Toshiba international lighting division smart home integration company anyCOMM and Research In Motion RIM maker of the PlayBook tablet

Label: 11, with a score of: 0.903647594013627
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The project will see Stratford homes and businesses fitted with Wi Fi enabled LED light bulb prototypes from Toshiba wirelessly networked and controlled via RIM PlayBook touchscreen loaded with anyCOMM home automation software

Label: 0, with a score of: 1

The tablet program follows investments in hybrid Internet infrastructure analogous to other utility and infrastructure networks such as electricity water natural gas and transportation

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When the Province of Ontario energy board mandated electrical utilities to switch to smart meters that would provide consumers with their hourly usage data Stratford opted for Wi Fi mesh canopy over Rhyzome kilometer loop of optical fiber woven through the city

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As result of this meter data backhaul system there is contiguous ubiquitous highspeed Internet access across the entire operating area

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The technology will start with on off and dimming commands for individual bulbs and is expected to evolve features such as smart wall plugs heating and cooling controls and home security

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Once the system is proven the larger plan is to deploy the LEDs and tablets across the city homes and businesses

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An integrated system also offers social engagement and two way communications with the city giving households access to online services and city information from school bus cancellations to emergency preparedness and disaster response

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Recognizing this vision for economic and social infrastructure the New York based think tank Intelligent Community Forum designated Stratford as one of the top seven intelligent communities worldwide in and ranking it among cities such as New York Seoul Stockholm and Taipei

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These in home systems will be integrated with Stratford citywide wireless smart meter system allowing customers to control their energy costs usage timing and conservation

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They will be able to access their BOX IBM Smart City Projects PART II THE PATH TO SUSTAINABILITY IBM is member of the Sustainable Cities Partnership and has been active in research and development of smart city technology partnering with cities across three continents Portland Oregon Understanding Connections between City Systems Today most cities are managed in silos but this approach does not mirror how cities function in reality

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Label: 17, with a score of: 0.159729139724303
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Through partnership with the City of Portland IBM has developed an approach to city planning that looks at city all at once and over time

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Label: 17, with a score of: 0.208963005302488
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IBM System Dynamics for Smarter Cities is systems thinking tool that helps city leaders learn how their city functions as an interconnected system of systems by exploring interactive visual maps and simulating macro level policy changes

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By enabling them to visualize how city systems work together the simulation model helps city leaders analyze policy decisions and their impact on citizens

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The simulation model examines the relationships that exist among city core systems such as the economy housing education public safety transportation health care government services and utilities allows city planners to see how city systems interact with and affect each other in order to improve long range city planning and help them become systems thinkers and enables municipal officials to create countless what if scenarios

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Portland is using the model to help create new year strategic plan for the city

Label: 11, with a score of: 0.983295634744901
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Rio de Janeiro Improving Emergency Response The City of Rio de Janeiro and IBM are collaborating on city operations center designed to improve emergency response coordination manage increased traffic flows and improve city services as the city prepares for hosting the World Cup and the Olympics

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Following series of floods and mudslides in April the Rio Operations Center was initially designed to improve city safety and responsiveness to incidents

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In IBM and the local government extended their collaboration with the announcement of an emergency alert system that will notify city officials and emergency personnel when changes occur in the flood and landslide forecast for the city

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In contrast to previous system in which notifications were manually relayed the new alert system is expected to drastically reduce the reaction times to emergency situations by using instantaneous mobile communications including automated email notifications and instant messaging to reach emergency personnel and citizens

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Currently the city operations center integrates and interconnects information from more than government departments to one centralized command center helping local government officials gather data across city operations to monitor and respond to problems more quickly and to predict potential problems that might emerge in order to minimize impact

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Over time the goal is to expand this center to also cover transportation public works and utilities

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Dubuque Iowa Promoting Sustainability Economic Growth and City Brand In The City of Dubuque and IBM came together to form Smarter Sustainable Dubuque SSD publicprivate partnership with the aim of leveraging smart technologies to improve sustainability and economic growth and development in Dubuque Iowa

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Label: 8, with a score of: 0.428554479210044
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Together IBM and the local government hope to make Dubuque one of the first smarter sustainable cities in North America and to develop new smart technologies and sustainability model that can be replicated globally in communities of and smaller where over percent of the population of the United States resides

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Label: 17, with a score of: 0.331658147614302
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Reflected in the design of SSD is the local government belief that the key to long term sustainability is to give consumers and businesses the information that they need to make informed decisions about how they consume resources like electricity water natural gas and oil

Label: 12, with a score of: 0.667797530128825
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The first project the Smarter Water Pilot enabled households to view their water usage on an hourly basis take advantage of water conservation tips compare their water usage performance against other households and be alerted if leaks were detected

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After the month pilot participants decreased their water use by

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Label: 12, with a score of: 0.310233790134587
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percent and leak detection and response was increased eight fold

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Other projects under SSD include Smarter Electricity Smarter Natural Gas and Smarter Travel Pilots

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Label: 13, with a score of: 0.394542796972612
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Stockholm Improving Traffic Flows and Decreasing Pollution Like many other cities in the world Stockholm is battling the problem of too many cars on too few roads with over half million cars traveling into the city every weekday

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By average commute times were up by percent from the year before

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Label: 13, with a score of: 0.616753303790897
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To combat this problem the Swedish National Road Administration and the Stockholm City Council announced in early trial congestion tax similar to the road charging systems in London Oslo and Singapore

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The goal was not only to reduce congestion but to also encourage ancillary benefits such as improving public transport and alleviating environmental damage

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The government plan is to devote revenue from the tax to the completion of ring road around the city

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The trial period ran from January to July and the tax was reinstated in by the then newly elected city government

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As related follow up project IBM is collaborating with KTH Royal Institute of Technology in Sweden to provide Stockholm residents and officials smarter way to manage and use transportation

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Researchers at KTH Royal Institute of Technology are using IBM streaming analytics technology to gather real time information from the GPS devices on nearly taxi cabs in the city

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This will be expanded to gather data from delivery trucks traffic sensors transit systems pollution monitors and weather information

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The data processed by IBM InfoSphere Streams software gives city officials and residents real time information on traffic flow travel times and the best commuting options

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Additional details and media coverage can be found at http www

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html BUILDING SUSTAINABILITY IN AN URBANIZING WORLD TABLE TechnologyBased Initiatives in cities Sector Energy Transport Water Actions Smart grid Description Sensors and instrumentation to improve distribution network efficiency in conjunction with smart metering helps match energy demand and supply Implemented Authorised or Awaiting Authorisation Building energy management system Occupants can automate the energy consuming systems in buildings Smart building sensors and controls Building sensors and controls allow for better use of buildings or prediction of faults Smart energy metering Automated meter reading enables utility and occupants to access information digitally Outdoor lighting smart controls Dimming and other controls enable greater energy efficiency Smart transport cards Ideally smart cards link multiple forms of transport and make it more convenient to use and for transport authorities to understand mobility patterns Car clubs Users can hire or share vehicles easily and will ideally not buy car but instead simply use one when it is convenient Cycle hire programs sharing programs Users can hire bicycles instead of driving Electric buses Buses that are more efficient and ideally run on renewable power Electric trains Trains that are more efficient and ideally run on renewable power Electric vehicles Vehicles that can become mobile storage for energy helping to balance peak demand Real time information for logistics Telematics and communications with drivers to optimise routes Real tme transport information Provides the basis for mobile applications for journey planning Real time transport displays Provides visibility to users and encourages uptake of public transportation Smart water metering Monitors and helps water managers reduce waste in the system saving per household Total Source CDP

Label: 7, with a score of: 0.573360938106326
WordScorePoS
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Label: 12, with a score of: 0.496280801279905
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Label: 11, with a score of: 0.343240249307545
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Label: 6, with a score of: 0.32046369622651
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Label: 17, with a score of: 0.304498894930194
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Buildings Buildings account for approximately percent of the world energy use UNEP and building related greenhouse gas emissions have been estimated at

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million metric tons of carbon dioxide equivalent in Levine et al

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Assuming that emissions will continue growing at high

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percent per year that figure could reach

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billion metric tons of carbon dioxide equivalent by UNEP

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Historically the majority of emissions from buildings have been generated in North America Europe and Central Asia but the total emissions from buildings in developing countries are expected to surpass these regions by Figure

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The long lifetime of buildings to years locks in their design and technical characteristics for decades

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But in this growth phase new buildings also provide opportunities to reduce energy consumption through the careful selection of construction materials building design equipment and appliances and during building operation

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PART II THE PATH TO SUSTAINABILITY IPCC High Growth Scenario FIG

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Carbon Dioxide Emissions from Buildings Source Reprinted from UNEP data from Levine et al

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Note Shown are carbon dioxide emissions from buildings including through the use of electricity

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Dark red historic emissions

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Light red projections

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Data for are adjusted to actual carbon dioxide emissions

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EECCA Countries of Eastern Europe the Caucasus and Central Asia

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Photo Curt Carnemark World Bank BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Indeed the building sector has greater potential than any other sector for significantly reducing greenhouse gas emissions Figure

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This means that with commercially available technologies energy consumption in both new and existing buildings can be cut by an estimated to percent with potential net profit during the building lifespan

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The potential for successful business in this sector cannot be underestimated

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Many countries and cities have tried to implement policies to reduce the greenhouse gas emissions of buildings but there are several bottlenecks

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First the building sector is highly fragmented from design through to the decommissioning phase

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No single policy framework would be able to affect all these phases

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In addition as discussed in Chapter the economic incentives for resource efficiency are poorly designed in particular split incentives Study and best practices in the United States show that just adjusting building operational practices can reduce energy use between and percent without requiring equipment upgrades or substantial retrofits

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See http esl

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tamu

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edu FIG

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Estimated Economic Mitigation Potential between building owners and tenants may prevent action and the costs and benefits of efficient solutions are not widely known or benchmarked

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Given the technical nature of green building the engineering and design professions have an opportunity to make available standardized technical notes that could be distributed among the builders and developers in cities around the world

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Despite the challenges the record of countries and cities in implementing legislation and changing behavior is encouraging Table

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WordScorePoS
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Some typical initiatives include Building codes Most developed countries have codes for new buildings that are performancebased for example they set maximum limit for the level of heat transfer through the building and require that all the equipment meet certain energy standards

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WordScorePoS
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WordScorePoS
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Label: 12, with a score of: 0.208120186038497
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initiative0NN
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The European Union has harmonized the standards for energy performance and certification in buildings

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WordScorePoS
standard0NN
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Label: 12, with a score of: 0.29756201925404
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WordScorePoS
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European Commission

Label: 14, with a score of: 1
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GtCO eq yr Non OECD EIT EIT OECD World Total < < < < < < Energy Supply Transport

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WordScorePoS
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WordScorePoS
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< < < < < < < < < < < < Buildings Industry Agriculture Forestry < < < Waste Total Sectoral Potential at

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WordScorePoS
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Label: 9, with a score of: 0.501552893258499
WordScorePoS
building0NN
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WordScorePoS
building0NN
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Label: 17, with a score of: 0.296804575485903
WordScorePoS
building0.149940347231585NN
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US tCO eq Source Adapted from Metz et al

Label: 6, with a score of: 0.73415387760794
WordScorePoS
source0.0613562156822703NN

Note Graph shows estimated economic mitigation potential using technologies and practices expected to be available in organized by sector and region

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WordScorePoS
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Label: 8, with a score of: 0.235957740325768
WordScorePoS
estimate0NN
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Label: 5, with a score of: 0.394237369261807
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Label: 9, with a score of: 0.364354584160787
WordScorePoS
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PART II THE PATH TO SUSTAINABILITY Appliance standards are in place in some countries for energy using products such as lighting heating and cooking equipment and personal computers

Label: 7, with a score of: 0.905566418483543
WordScorePoS
sustainability0NN
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Label: 12, with a score of: 0.355290892345143
WordScorePoS
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The Top Runner program in Japan resulted in energy efficiency improvement of more than percent Geller et al

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WordScorePoS
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Label: 12, with a score of: 0.216595844857663
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Standards are cost effective because they reduce transaction costs for consumers and producers

Label: 12, with a score of: 0.865433514048857
WordScorePoS
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Label: 10, with a score of: 0.406502796262839
WordScorePoS
standard0NN
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Encouraging off grid applications of renewable energy technologies in buildings For example solar water heaters have been mandatory for new buildings in Spain since and Australia since

Label: 7, with a score of: 0.605500422246131
WordScorePoS
encourage0VB
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Label: 6, with a score of: 0.544598950900516
WordScorePoS
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Label: 17, with a score of: 0.350566071881272
WordScorePoS
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Label: 12, with a score of: 0.310350921496351
WordScorePoS
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Baden Wurttemberg in Germany has enacted legislation imposing rules on new buildings regarding how much of their energy should come from renewable sources

Label: 7, with a score of: 0.800784326287415
WordScorePoS
legislation0NN
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Label: 16, with a score of: 0.397600565694141
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Label: 17, with a score of: 0.289191378844077
WordScorePoS
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Label: 12, with a score of: 0.185155428406972
WordScorePoS
legislation0NN
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Germany Ireland Japan Luxemburg and South Africa all use national subsidies to encourage public structures to use renewable energy

Label: 7, with a score of: 0.823108175842225
WordScorePoS
south0RB
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national0JJ
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Label: 12, with a score of: 0.386824545436039
WordScorePoS
south0RB
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Label: 14, with a score of: 0.209790700623117
WordScorePoS
south0RB
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Disseminating renewable energy appliances In developing countries particularly those with low electrification rates there has been great progress

Label: 7, with a score of: 0.931266399154226
WordScorePoS
renewable0.158624482267508JJ
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Label: 12, with a score of: 0.213727383634744
WordScorePoS
renewable0.0234335422829798JJ
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A major Dutch and German project initiated in has provided million people with access to improved cook stoves and electricity

Label: 6, with a score of: 0.534946927614382
WordScorePoS
major0JJ
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WordScorePoS
major0.0325677023224526JJ
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Many projects financed by the World Bank and regional development banks have also had clear impact on the use of renewable fuels

Label: 7, with a score of: 0.469372770812181
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project0NN
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Zero carbon buildings where energy provided by on site renewable sources is equal to the energy used by the building

Label: 7, with a score of: 0.878667499995348
WordScorePoS
carbon0.0625160381065121NN
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carbon0NN
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Label: 12, with a score of: 0.206813333657143
WordScorePoS
carbon0NN
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Zero energy buildings are connected to the main electricity grid but have zero net consumption because they produce more energy in the summer and consume more in the winter

Label: 7, with a score of: 0.792690752389222
WordScorePoS
energy0.494617163780726NN
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Label: 12, with a score of: 0.508186749751414
WordScorePoS
energy0.133960988253756NN
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WordScorePoS
energy0.0109843026967392NN
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Label: 8, with a score of: 0.111404229955389
WordScorePoS
energy0NN
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Interesting projects have been piloted notably Worldwide Federation for Nature WFN Photo Bigstock Mandatory building energy audits In the United States savings identified after energy auditing can average percent in cooking percent in heating and percent overall energy savings

Label: 7, with a score of: 0.903125264100758
WordScorePoS
project0NN
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Label: 12, with a score of: 0.329694027346601
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Label: 13, with a score of: 0.166630857949489
WordScorePoS
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Label: 17, with a score of: 0.114860349827811
WordScorePoS
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Label: 10, with a score of: 0.0765939474720343
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project0NN
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zero energy housing project in the Netherlands and the Malaysia Energy Center Pusar Tenaga Malaysia headquarters in Kuala Lumpur

Label: 7, with a score of: 0.898009635020202
WordScorePoS
energy0.494617163780726NN
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Label: 12, with a score of: 0.30351909816624
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energy0.133960988253756NN
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Label: 11, with a score of: 0.288604866598469
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The town of Pedra Branca Brazil has residents aiming to become zero carbon community

Label: 13, with a score of: 0.626864675457454
WordScorePoS
aim0NN
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In Germany passive building technologies are spreading rapidly while in France the Grenelle de Environment in France recommended that all new housing be passive or energy positive by USGBC

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Label: 11, with a score of: 0.494314444433666
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Label: 17, with a score of: 0.314555714078497
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Label: 12, with a score of: 0.248294445527617
WordScorePoS
building0NN
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Rapid deployment neighborhoods From to an estimated million people per year were added to the world urban slum population out of total million new urban dwellers per year in the developing world UN Habitat

Label: 11, with a score of: 0.905144711918163
WordScorePoS
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Suitable housing must be provided rapidly to prevent slum formation

Label: 11, with a score of: 0.965776186858634
WordScorePoS
housing0.219415728260093NN
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This can be accomplished by off site factory based parallel assembly of building subsystems structural floor and façade

Label: 17, with a score of: 0.704388248062403
WordScorePoS
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This is not only faster and more efficient construction method it also reduces construction waste and vehicle travel to and from the building site as deliveries are made only when components are completed and fewer workers are required on site during assembly

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Label: 17, with a score of: 0.285768672312485
WordScorePoS
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Label: 8, with a score of: 0.379059692986225
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Pilots of the rapid deployment neighborhood system have been carried out in Bangladesh India Nigeria and Pakistan

Label: 0, with a score of: 1

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Program Categories Program Examples TABLE Examples of Policies to Improve Building Energy Efficiency Policies and Targets Energy saving goals tracking and reporting progress government organisation lead responsibility for energy savings interagency committees etc

Label: 7, with a score of: 0.814771233359712
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Budget policies e

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Label: 1, with a score of: 0.337763538373769
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policy0.0450489732120807NN

life cycle costing separate budget line for energy energy cost saving shared with agencies Agentina reporting Dominican Republic goals Ecuador goals Mexico saving goals and reporting requirements Philippines GEMP goals Energy Saving Capital Projects Energy audits Retrofit projects lighting HVAC building envelope controls Financing third party ESCO funding loan funds leasing Efficiency standards guidelines for new buildings Design assistance software tools architect training New technology demonstrations showcase facilities Public services efficient systems and equipment water supply and treatment street lighting LED traffic signals Brazil low interest loans to retrofit public buildings Colombia and Argentina street lighting Mexico Web based lighting audits Public Buildings and APF Russian Federation pilot audits and retrofits Facilities Operation and Maintenance Building system commissioning pre occupancy continuous energy metering monitoring benchmarking operator feedback Facility manager training and certification Operator incentives and recognition awards Employee information and outreach campaigns M for government vehicles promote ridesharing and transit Dominican Republic Mexico building M operator training Ports of Attention for outreach technical assistance Thailand mandatory measures in public buildings Purchasing Energy efficient Products Speciy efficient building equipment office equipment motors lighting appliances etc

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Label: 12, with a score of: 0.417095994410662
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Efficient and alternative fuel vehicles for government fleets Green power purchasing Republic of Korea Philippines GEMP Source Adapted from Hallegate et al

Label: 12, with a score of: 0.794035114141209
WordScorePoS
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Public policies for green buildings generally include three options revising zoning codes incentivizing developers and making public access to finance conditional on sustainability criteria

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WordScorePoS
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Label: 11, with a score of: 0.464039300384425
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The recent report Building the Green Economy from the Ground Up USGBC and Green Building Council Brasil suggests four pillars of intervention fostering green communities and neighborhoods achieving sustainable and affordable housing building green schools and pursuing resilience as part of the sustainable built environment

Label: 11, with a score of: 0.585693664643432
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Label: 4, with a score of: 0.273473157697497
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report0NN
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At the community or neighborhood level many tools exist to help assess and increase energy efficiency for example the LEED for Neighborhood Design rating system from the U

Label: 7, with a score of: 0.8016361941166
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Label: 12, with a score of: 0.39724329439967
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Green Building Council USGBC and ICLEI STAR Community Index

Label: 17, with a score of: 0.609717997288229
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Label: 11, with a score of: 0.485861258651406
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Label: 16, with a score of: 0.44645411538362
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In the building industry there is consensus on the attractive market potential for efficient new buildings and retrofitting old buildings Boxes and and growing number of building owners are combating rising energy prices by pursuing energy efficiency

Label: 7, with a score of: 0.660553273932982
WordScorePoS
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Label: 17, with a score of: 0.435621245442525
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Label: 9, with a score of: 0.276563640330117
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Label: 12, with a score of: 0.212938721150371
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The Fifth Annual Global Energy Efficiency Survey Johnson Controls IFMA and ULI which polled building owners around the world shows that energy cost savings http www

Label: 7, with a score of: 0.890554900934138
WordScorePoS
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Label: 12, with a score of: 0.282466507688749
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Label: 17, with a score of: 0.171817733766522
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icleiusa

Label: 0, with a score of: 1

org sustainability star community index PART II THE PATH TO SUSTAINABILITY The survey indicates that percent of building owners have set energy reduction goals targeting percent reductions on average and nearly percent have achieved at least one green building certification which is twice as many as the previous year

Label: 17, with a score of: 0.54418247574207
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Label: 7, with a score of: 0.466353108424902
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Label: 13, with a score of: 0.359498296450582
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Label: 10, with a score of: 0.325356887126268
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Label: 12, with a score of: 0.303053360753667
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An additional percent have incorporated green building elements such as lighting heating ventilation air conditioning and controls improvements which continue to be the most popular energy efficiency improvements percent of the North American building owners plan to include green building elements in their facility plans in the next months

Label: 7, with a score of: 0.642929672708172
WordScorePoS
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Label: 17, with a score of: 0.419933320785646
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Label: 12, with a score of: 0.244208601352388
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Label: 11, with a score of: 0.457604054002148
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Efficiency in buildings remains the top global strategy for reducing greenhouse gas emissions in the United States Europe China and India

Label: 13, with a score of: 0.715629965943316
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Label: 7, with a score of: 0.370917875876914
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From to BOX The Commercial Building Retrofit Market Potential 'The market potential for commercial building retrofits is projected to be billion over the next years or roughly billion annually

Label: 17, with a score of: 0.624940845542856
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'Annual energy costs in the existing commercial building stock total billion or roughly

Label: 7, with a score of: 0.745286868835356
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Label: 12, with a score of: 0.458723324473416
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Label: 17, with a score of: 0.300626585654263
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per square foot over billion square feet

Label: 0, with a score of: 1

'Achievable energy savings at any one building may typically range from percent to percent depending on building age type design condition and maintenance

Label: 7, with a score of: 0.658229931405945
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Label: 17, with a score of: 0.434089049016957
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Label: 12, with a score of: 0.232808063799788
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'Achievable energy savings across the existing commercial building stock is estimated to potentially reach percent

Label: 7, with a score of: 0.717341343993214
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Label: 12, with a score of: 0.457662304501215
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Label: 17, with a score of: 0.320286301796735
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Source Johnson Controls

Label: 1, with a score of: 0.648312140864107
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Label: 6, with a score of: 0.449891876836938
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Label: 15, with a score of: 0.364031495531375
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Label: 5, with a score of: 0.34140720000939
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Reprinted from Johnson Controls IFMA and ULI

Label: 1, with a score of: 0.730398396150546
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Photo Shutterstock government incentives and enhanced public image are driving energy efficiency in buildings to new heights

Label: 7, with a score of: 0.713307139158444
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Label: 17, with a score of: 0.480732676393052
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Label: 12, with a score of: 0.226218881541825
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The expected rise of energy costs is especially motivating as percent of respondents expect double digit energy price increases over

Label: 7, with a score of: 0.879736946385562
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Label: 12, with a score of: 0.325175445523289
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD site see Box

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Label: 16, with a score of: 0.451970375561086
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Label: 11, with a score of: 0.333298615483867
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More refined analyses consider the energy consumed in all stages of building life cycle including resource extraction manufacturing of building elements construction use and disposal and decommissioning

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WordScorePoS
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Label: 9, with a score of: 0.415768119618385
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Label: 12, with a score of: 0.389678432223734
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Label: 17, with a score of: 0.387817045415794
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Recent studies demonstrate that embodied energy and operational energy contribute to the overall energy balance of building in almost equal shares and that combined they are responsible for about percent of global carbon dioxide emissions

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Label: 12, with a score of: 0.28294222512524
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Label: 13, with a score of: 0.194554838918386
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An integrated approach tackling both types of energy use is urgently needed to reduce building related emissions

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Label: 11, with a score of: 0.311349369385284
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Embodied Energy and Historic Buildings Most of the current debate on energy in buildings focuses on operational energy the energy needed for building occupancy and use heating cooling and lighting for example

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But buildings are actually associated with two types of energy embodied and operational

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Embodied energy is the energy consumed by all of the processes related to the construction of building including the mining and processing of natural resources to manufacture building materials transport product delivery and final assembly at the construction This section has been adapted with permission from Licciardi

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BOX Case Study Reducing Operational Energy in the Empire State Building In sustainability experts joined forces to retrofit the Empire State Building in New York using an innovative design process and state of the art tools

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The guiding goal was to use the most cost effective measures to produce the most energy savings

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After comprehensive research it was found that rather than replacing the windows and purchasing new ones from distant factories it was more energy efficient and cost effective to upgrade the dual pane existing windows by conserving frames and glass and upgrading them to super insulating glass units in dedicated processing space located on site

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As result percent of the frames and glass of the Empire State Building were conserved and reused

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The project was completed in October as one of eight measures in an innovative energy retrofit of the building

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The upgraded windows are expected to pay for themselves in three years

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The project impacts include conserving embodied energy of existing windows made of aluminum and therefore having very high PER reducing operational energy use by percent saving

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million per year in operational energy costs and saving metric tons of carbon dioxide emissions over the next years equivalent to the annual emissions of cars

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Photo istockphoto Measuring Embodied Energy Process energy requirement PER is the energy directly related to the manufacture of building materials

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It includes the energy consumed in transporting the raw materials to the factory as well as material production but not the energy consumed in transporting the final product to the building site and assembling it which are the remaining components of GER unaccounted for by the PER

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Label: 11, with a score of: 0.34068398639146
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In general PER accounts for percent of GER and PER tables are widely available for variety of building materials

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Both GER and PER are measured in megajoules of energy needed to make kilogram of product

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The consensus on PER data for wide range of materials is extremely encouraging and the data can already be used by stakeholders designers and developers to make effective decisions in urban development especially to advocate conservation and adaptive reuse of existing built assets

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As an example producing kilogram of aluminum requires megajoules whereas producing kilogram of bricks can require as little as megajoules

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In practice this means that choosing materials with lower PER dramatically reduces the embodied energy of building

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It also means that by conserving and adaptively reusing existing buildings the total PER that is the cumulative PER of their constituent materials does not have to be discarded which substantially reduces greenhouse gas emissions

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Photo Bigstock Gross energy requirement GER is measure of the true embodied energy of material which would ideally include all of the steps from mining to assembly

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Research has made significant progress toward assessing GER and number of tools have been widely tested to measure the subset of GER known as the process energy requirement PER

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BOX While operational energy can be easily determined by measuring what is needed to operate building embodied energy is less apparent because it is embedded in the various steps of building construction and material production

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Various tools exist however to analyze embodied energy and they are becoming increasingly refined

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The SBTool standard the U

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Code for Sustainable Homes and the LEED Rating System from the USGBC already take embodied energy along with other factors into consideration when assessing building environmental impact

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These standards are very effective tools to promote both energy efficiency and the conservation of embodied energy in existing and historic built assets

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For instance under the LEED system projects can earn one credit for reusing percent of the core and shell of an existing building

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Life cycle assessment LCA examines impacts during building entire life rather than focusing on one particular stage

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WordScorePoS
life0NN
cycle0NN
impact0NN
building0.149940347231585NN
focus0NN
stage0NN

Label: 12, with a score of: 0.607299210536505
WordScorePoS
life0.0250071479597636NN
cycle0.108503934593034NN
impact0.0866018859977851NN
building0NN
focus0NN
stage0NN

Label: 11, with a score of: 0.381516598848938
WordScorePoS
life0.0112376090361851NN
cycle0NN
impact0.0194583992186006NN
building0.0747035830159326NN
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stage0NN

Label: 16, with a score of: 0.342865865584307
WordScorePoS
life0NN
cycle0NN
impact0.0285977592242194NN
building0.109790895760528NN
focus0NN
stage0NN

Unlike traditional embodied energy calculations LCA provides an assessment of direct and indirect environmental impacts associated with building by quantifying energy material use and environmental impacts at each stage of the life cycle including resource extraction goods manufacturing construction use and disposal and decommissioning

Label: 7, with a score of: 0.669741529969561
WordScorePoS
traditional0JJ
energy0.494617163780726NN
direct0JJ
environmental0JJ
impact0NN
building0NN
material0NN
stage0NN
life0.0188084899376888NN
cycle0NN
include0VB
resource0NN
manufacture0NN

Label: 12, with a score of: 0.488976581893828
WordScorePoS
traditional0JJ
energy0.133960988253756NN
direct0JJ
environmental0.0598270388037045JJ
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building0NN
material0NN
stage0NN
life0.0250071479597636NN
cycle0.108503934593034NN
include0VB
resource0.0416785799329393NN
manufacture0NN

Label: 9, with a score of: 0.334804573029521
WordScorePoS
traditional0JJ
energy0.0540565419125769NN
direct0JJ
environmental0.0221298700466866JJ
impact0.0160169090870187NN
building0NN
material0NN
stage0.0602029956716497NN
life0NN
cycle0NN
include0VB
resource0.0185001613201543NN
manufacture0.240811982686599NN

Label: 17, with a score of: 0.159096724072609
WordScorePoS
traditional0JJ
energy0.0109843026967392NN
direct0.0299586785922821JJ
environmental0JJ
impact0NN
building0.149940347231585NN
material0NN
stage0NN
life0NN
cycle0NN
include0VB
resource0.0375923735942499NN
manufacture0NN

Label: 11, with a score of: 0.316172562467386
WordScorePoS
traditional0JJ
energy0.0492535935240667NN
direct0.04477822029736JJ
environmental0.0537696560159885JJ
impact0.0194583992186006NN
building0.0747035830159326NN
material0.055245183797332NN
stage0NN
life0.0112376090361851NN
cycle0NN
include0VB
resource0.0449504361447405NN
manufacture0NN

LCA makes an even stronger case for conserving and adaptively reusing historic built assets and is the basis for the distribution of points under the updated LEED standards

Label: 0, with a score of: 1

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Embodied energy can be saved in two ways by reducing the energy used to produce and assemble materials in new buildings and by conserving and adaptively reusing existing buildings

Label: 7, with a score of: 0.707804520047835
WordScorePoS
building0NN
sustainability0NN
world0.00261936653839306NN
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reduce0.00838876601975825VB
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material0NN
conserve0VB
reuse0NN
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Label: 17, with a score of: 0.407745263314486
WordScorePoS
building0.149940347231585NN
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world0.00418824502414673NN
energy0.0109843026967392NN
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produce0VB
material0NN
conserve0VB
reuse0NN
exist0.0599173571845643VB

Label: 12, with a score of: 0.31366605584175
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
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save0IN
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material0NN
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reuse0.0409791940864438NN
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Label: 11, with a score of: 0.317691593328966
WordScorePoS
building0.0747035830159326NN
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world0.00626002771693937NN
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save0IN
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exist0.04477822029736VB

In both cases international practice has demonstrated that it is most important to focus on long lived durable buildings which store the most energy over time

Label: 7, with a score of: 0.753433077285074
WordScorePoS
international0.00540783824576293JJ
practice0NN
focus0NN
live0VB
building0NN
energy0.494617163780726NN
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Label: 12, with a score of: 0.336992094383555
WordScorePoS
international0.0023966944644375JJ
practice0.0703006268489394NN
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live0VB
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energy0.133960988253756NN
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Label: 17, with a score of: 0.25225156442952
WordScorePoS
international0.00648516099948979JJ
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live0VB
building0.149940347231585NN
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In fact the best way to save embodied energy is to make buildings last longer

Label: 7, with a score of: 0.871903476755425
WordScorePoS
save0IN
energy0.494617163780726NN
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Label: 17, with a score of: 0.283675480841903
WordScorePoS
save0IN
energy0.0109843026967392NN
building0.149940347231585NN

Label: 12, with a score of: 0.236144355596651
WordScorePoS
save0IN
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In the case of new buildings higher embodied energy can be justified only if it contributes to lower operational energy

Label: 7, with a score of: 0.906499567002749
WordScorePoS
building0NN
energy0.494617163780726NN
contribute0VB

Label: 12, with a score of: 0.296292662500255
WordScorePoS
building0NN
energy0.133960988253756NN
contribute0.0554128417527413VB

Label: 17, with a score of: 0.180431332380858
WordScorePoS
building0.149940347231585NN
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contribute0.0249900578719309VB

For example high thermal mass associated with high embodied energy can significantly reduce heating and cooling needs in well designed and insulated passive solar houses

Label: 7, with a score of: 0.956864692452043
WordScorePoS
energy0.494617163780726NN
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heating0.122412709674631NN

Label: 12, with a score of: 0.239083344698646
WordScorePoS
energy0.133960988253756NN
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heating0NN

However priority should go to conserving embodied energy in existing buildings considering the variety of options to adapt older structures for changing needs Box

Label: 7, with a score of: 0.691785205063337
WordScorePoS
priority0NN
conserve0VB
energy0.494617163780726NN
exist0VB
building0NN
variety0NN
structure0NN
change0.105749654845006NN

Label: 13, with a score of: 0.478045007040041
WordScorePoS
priority0NN
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energy0.0123065238088614NN
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building0NN
variety0NN
structure0NN
change0.402565546215053NN

Label: 17, with a score of: 0.310853948820191
WordScorePoS
priority0NN
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energy0.0109843026967392NN
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building0.149940347231585NN
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Label: 12, with a score of: 0.239851293718941
WordScorePoS
priority0.0409791940864438NN
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energy0.133960988253756NN
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building0NN
variety0NN
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Demolishing BOX Case Study Reusing Historic Mansions in Qufu and Zoucheng An innovative project currently under preparation financed by the World Bank million and China million will regenerate the two historic cities of Qufu and Zoucheng in Shandong Province

Label: 11, with a score of: 0.92692400362773
WordScorePoS
study0NN
reuse0NN
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world0.00626002771693937NN
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In light of the importance of embodied energy one project component focuses on the conservation and adaptive reuse of two large historic buildings that are currently underutilized

Label: 7, with a score of: 0.734323952642264
WordScorePoS
energy0.494617163780726NN
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conservation0NN
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Label: 12, with a score of: 0.358345999282169
WordScorePoS
energy0.133960988253756NN
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reuse0.0409791940864438NN
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Label: 17, with a score of: 0.238913716842482
WordScorePoS
energy0.0109843026967392NN
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conservation0NN
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The Confucius and Mencius mansions are vast former residences that today are mostly abandoned

Label: 1, with a score of: 0.899723761082546
WordScorePoS
vast0JJ
abandon0.120266261535276VB

The buildings will be revitalized to host number of new productive functions from knowledge centers to growth poles for sustainable tourism

Label: 8, with a score of: 0.645642363597448
WordScorePoS
building0NN
revitalize0VB
productive0.0985415302158139JJ
function0NN
knowledge0NN
growth0.208565600657229NN
sustainable0.00688808874408283JJ
tourism0.049270765107907NN

Label: 17, with a score of: 0.528457455189529
WordScorePoS
building0.149940347231585NN
revitalize0.048933054487825VB
productive0JJ
function0.036961556179878NN
knowledge0.0499801157438617NN
growth0NN
sustainable0.0115176738164035JJ
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Label: 2, with a score of: 0.362648584937699
WordScorePoS
building0NN
revitalize0VB
productive0.0714379839896614JJ
function0.0440683498519077NN
knowledge0.0595900566394012NN
growth0.0252000451111224NN
sustainable0.00374515576585943JJ
tourism0NN

The physical conservation of the mansions will maximize the use of lowimpact traditional techniques and locally available building materials to reduce the additional embodied energy the project will create

Label: 7, with a score of: 0.695480773737082
WordScorePoS
conservation0NN
traditional0JJ
building0NN
material0NN
reduce0.00838876601975825VB
additional0JJ
energy0.494617163780726NN
project0NN
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Label: 12, with a score of: 0.34031370472943
WordScorePoS
conservation0NN
traditional0JJ
building0NN
material0NN
reduce0.022306853314744VB
additional0JJ
energy0.133960988253756NN
project0.0664302389559493NN
create0.0234335422829798VB

Label: 17, with a score of: 0.278243668925915
WordScorePoS
conservation0NN
traditional0JJ
building0.149940347231585NN
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reduce0.00335331148065841VB
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energy0.0109843026967392NN
project0NN
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Label: 11, with a score of: 0.319173643153059
WordScorePoS
conservation0NN
traditional0JJ
building0.0747035830159326NN
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reduce0.0200483235258943VB
additional0JJ
energy0.0492535935240667NN
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create0.0315914058171786VB

existing buildings to construct new ones generates additional greenhouse gas emissions during demolition debris and waste disposal production of new materials and assembly of replacement buildings

Label: 12, with a score of: 0.476283725421116
WordScorePoS
exist0.0332151194779746VB
building0NN
generate0VB
additional0JJ
greenhouse0.0332151194779746NN
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debris0NN
waste0.138532104381853NN
production0.118934074671047NN
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Label: 17, with a score of: 0.458082367061792
WordScorePoS
exist0.0599173571845643VB
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greenhouse0NN
gas0NN
emission0NN
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production0NN
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Label: 13, with a score of: 0.381289738245379
WordScorePoS
exist0VB
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greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
debris0NN
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production0NN
material0NN

Label: 11, with a score of: 0.374231996371954
WordScorePoS
exist0.04477822029736VB
building0.0747035830159326NN
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greenhouse0NN
gas0NN
emission0.0373517915079663NN
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Label: 7, with a score of: 0.361677859926946
WordScorePoS
exist0VB
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greenhouse0.0749457204978913NN
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debris0NN
waste0.0625160381065121NN
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material0NN

There is common misperception that historic buildings are energy inefficient and that the environmental impacts of demolition and new construction are easily outweighed by the energy savings from contemporary green buildings

Label: 7, with a score of: 0.787400826005671
WordScorePoS
common0JJ
building0NN
energy0.494617163780726NN
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Label: 12, with a score of: 0.356249115746385
WordScorePoS
common0JJ
building0NN
energy0.133960988253756NN
environmental0.0598270388037045JJ
impact0.0866018859977851NN
green0.0332151194779746JJ

Label: 17, with a score of: 0.256182380145648
WordScorePoS
common0JJ
building0.149940347231585NN
energy0.0109843026967392NN
environmental0JJ
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Label: 11, with a score of: 0.3352353424632
WordScorePoS
common0.055245183797332JJ
building0.0747035830159326NN
energy0.0492535935240667NN
environmental0.0537696560159885JJ
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green0.04477822029736JJ

In fact recent research has found that conserving reusing and retrofitting existing buildings are often better options

Label: 17, with a score of: 0.601508428923524
WordScorePoS
research0NN
conserve0VB
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building0.149940347231585NN

Label: 6, with a score of: 0.395910484455117
WordScorePoS
research0NN
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building0.0348974218717993NN

Label: 11, with a score of: 0.342466872950305
WordScorePoS
research0NN
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exist0.04477822029736VB
building0.0747035830159326NN

Label: 16, with a score of: 0.314690134454457
WordScorePoS
research0NN
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This saves the embodied energy used in the construction of older buildings and in addition buildings constructed before the advent of cheap fossil fuels actually operate more efficiently than most late th century buildings according to groundbreaking study of the U

Label: 7, with a score of: 0.664178452404635
WordScorePoS
save0IN
energy0.494617163780726NN
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Label: 17, with a score of: 0.462302687270184
WordScorePoS
save0IN
energy0.0109843026967392NN
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Label: 12, with a score of: 0.263259936429659
WordScorePoS
save0IN
energy0.133960988253756NN
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Label: 13, with a score of: 0.232352313471496
WordScorePoS
save0IN
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Energy Information Administration

Label: 7, with a score of: 0.910988838880056
WordScorePoS
energy0.494617163780726NN
information0NN

Label: 12, with a score of: 0.314020186103261
WordScorePoS
energy0.133960988253756NN
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The study which has since been followed by number of similar investigations worldwide concluded that contemporary highly energyefficient buildings require the same operational energy as historic assets built before the Figure

Label: 7, with a score of: 0.74455692013291
WordScorePoS
study0NN
worldwide0RB
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require0VB
energy0.494617163780726NN
asset0NN
build0VB
figure0NN

Label: 17, with a score of: 0.349295661641265
WordScorePoS
study0NN
worldwide0RB
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building0.149940347231585NN
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build0.0499801157438617VB
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Label: 12, with a score of: 0.272204045535113
WordScorePoS
study0NN
worldwide0RB
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require0.0468670845659596VB
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This finding is based on simple analysis of the operational energy needed for similar categories of existing buildings still in use by year of construction

Label: 7, with a score of: 0.767499404586714
WordScorePoS
base0NN
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energy0.494617163780726NN
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Label: 17, with a score of: 0.446521806119983
WordScorePoS
base0.017987180284335NN
simple0.048933054487825JJ
energy0.0109843026967392NN
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building0.149940347231585NN

Label: 12, with a score of: 0.290352443360762
WordScorePoS
base0.0199423462679015NN
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Only assets built in the second half of the th century are energy inefficient result of the low cost of energy at the time of their construction

Label: 7, with a score of: 0.881740160497529
WordScorePoS
asset0NN
build0VB
century0NN
energy0.494617163780726NN
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Label: 12, with a score of: 0.278648548135752
WordScorePoS
asset0NN
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century0NN
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Label: 13, with a score of: 0.242358248952226
WordScorePoS
asset0NN
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century0.219293228426234NN
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In contrast earlier building designers and developers faced very high energy costs and addressed this in their designs and construction methods

Label: 7, with a score of: 0.790844594754618
WordScorePoS
building0NN
face0NN
energy0.494617163780726NN
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Label: 12, with a score of: 0.258490416469301
WordScorePoS
building0NN
face0NN
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cost0.0277064208763707NN
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Label: 17, with a score of: 0.257302816962119
WordScorePoS
building0.149940347231585NN
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Generally historic buildings have thick solid walls with high thermal mass that reduces the amount of operational energy needed for heating and cooling

Label: 7, with a score of: 0.870757170940888
WordScorePoS
building0NN
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energy0.494617163780726NN
heating0.122412709674631NN

Label: 17, with a score of: 0.228720738231394
WordScorePoS
building0.149940347231585NN
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Label: 12, with a score of: 0.21756841744823
WordScorePoS
building0NN
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Moreover buildings designed before the widespread use of electricity feature windows designed for natural light and ventilation as well as shaded porches and other details to reduce PART II THE PATH TO SUSTAINABILITY Average Annual Energy Consumption in U

Label: 12, with a score of: 0.609525458885586
WordScorePoS
building0NN
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Label: 7, with a score of: 0.593931545089792
WordScorePoS
building0NN
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Label: 17, with a score of: 0.211348991546749
WordScorePoS
building0.149940347231585NN
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Label: 13, with a score of: 0.208866762042838
WordScorePoS
building0NN
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natural0.0171695137813101JJ
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average0.1656430212089NN
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consumption0NN

Label: 10, with a score of: 0.186568682361756
WordScorePoS
building0NN
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Label: 8, with a score of: 0.161029783300965
WordScorePoS
building0NN
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energy0NN
consumption0.147812295323721NN

Commercial Buildings By Year of Construction Btu Ft FIG

Label: 17, with a score of: 0.712566012057575
WordScorePoS
commercial0JJ
building0.149940347231585NN

Label: 16, with a score of: 0.521762568893323
WordScorePoS
commercial0JJ
building0.109790895760528NN

Label: 11, with a score of: 0.355016079520335
WordScorePoS
commercial0JJ
building0.0747035830159326NN

Energy Consumption in U

Label: 7, with a score of: 0.69481362444563
WordScorePoS
energy0.494617163780726NN
consumption0NN

Label: 12, with a score of: 0.654771243311216
WordScorePoS
energy0.133960988253756NN
consumption0.332151194779746NN

Label: 8, with a score of: 0.207639370753888
WordScorePoS
energy0NN
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Commercial Buildings of Different Ages Before Source Adapted from Baeumler et al

Label: 17, with a score of: 0.572305819734362
WordScorePoS
commercial0JJ
building0.149940347231585NN
age0.0153248097886055NN
source0.0109843026967392NN

Label: 1, with a score of: 0.454571460801409
WordScorePoS
commercial0JJ
building0NN
age0.112994637345193NN
source0.0269969049497485NN

Label: 16, with a score of: 0.356505879215262
WordScorePoS
commercial0JJ
building0.109790895760528NN
age0NN
source0NN

Label: 6, with a score of: 0.312548571957435
WordScorePoS
commercial0JJ
building0.0348974218717993NN
age0NN
source0.0613562156822703NN

solar gain

Label: 12, with a score of: 0.735021154461331
WordScorePoS
gain0.0664302389559493NN

Label: 11, with a score of: 0.495451163430472
WordScorePoS
gain0.04477822029736NN

Label: 6, with a score of: 0.462895287456812
WordScorePoS
gain0.041835863322702NN

In the past designers and developers also paid close attention to location orientation and landscaping as methods for maximizing sun exposure during the winter months and minimizing it during warmer months that is passive heating and cooling systems

Label: 0, with a score of: 1

Historic buildings are also mostly located in densely built areas that were the norm in the era before cars proliferated and urban sprawl became prevalent and so they gain the benefits of compactness discussed in Chapter

Label: 11, with a score of: 0.811296233371301
WordScorePoS
building0.0747035830159326NN
build0VB
car0NN
urban0.27622591898666JJ
gain0.04477822029736NN

Label: 17, with a score of: 0.409885148672637
WordScorePoS
building0.149940347231585NN
build0.0499801157438617VB
car0NN
urban0JJ
gain0NN

District energy systems can be used for power generation as China has been doing for decades which also creates substantial carbon savings

Label: 7, with a score of: 0.733474615218183
WordScorePoS
energy0.494617163780726NN
system0NN
power0NN
generation0NN
decade0NN
create0VB
substantial0JJ
carbon0.0625160381065121NN

Label: 12, with a score of: 0.358840105477841
WordScorePoS
energy0.133960988253756NN
system0NN
power0NN
generation0.0819583881728876NN
decade0NN
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substantial0.0332151194779746JJ
carbon0NN

Label: 9, with a score of: 0.319238590819245
WordScorePoS
energy0.0540565419125769NN
system0NN
power0.0454743001434393NN
generation0.0909486002868787NN
decade0NN
create0.0520080474473126VB
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carbon0NN

Municipal infrastructure requirements for roads sewers communication power and water are also reduced by high density developments which is where most historic buildings are located

Label: 6, with a score of: 0.643764546021758
WordScorePoS
municipal0JJ
infrastructure0.0181797964452808NN
road0NN
communication0NN
power0NN
water0.556409482163189NN
reduce0.00936547854413134VB
density0NN
development0NN
building0.0348974218717993NN

Label: 9, with a score of: 0.455097564155912
WordScorePoS
municipal0JJ
infrastructure0.192202909044225NN
road0.0368585655748969NN
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reduce0VB
density0NN
development0.0330049940546426NN
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Label: 17, with a score of: 0.312285041251223
WordScorePoS
municipal0JJ
infrastructure0.0130185595639838NN
road0NN
communication0.0499801157438617NN
power0.036961556179878NN
water0NN
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density0NN
development0.0469463607292177NN
building0.149940347231585NN

Label: 12, with a score of: 0.270859096269282
WordScorePoS
municipal0JJ
infrastructure0.028867295332595NN
road0NN
communication0NN
power0NN
water0.186896403054502NN
reduce0.022306853314744VB
density0NN
development0.022306853314744NN
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Label: 11, with a score of: 0.375637174211849
WordScorePoS
municipal0.0731385760866977JJ
infrastructure0.0194583992186006NN
road0.04477822029736NN
communication0NN
power0NN
water0.0458109463416786NN
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density0.0731385760866977NN
development0.0100241617629471NN
building0.0747035830159326NN

Thus the adaptive reuse and repurposing of historic buildings together with the provision of appropriate retrofit measures can contribute to significant energy and resource conservation as well as cultural heritage preservation

Label: 7, with a score of: 0.598710998712295
WordScorePoS
adaptive0JJ
reuse0NN
building0NN
provision0NN
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contribute0VB
energy0.494617163780726NN
resource0NN
conservation0NN
cultural0JJ
heritage0NN

Label: 12, with a score of: 0.329281564114595
WordScorePoS
adaptive0JJ
reuse0.0409791940864438NN
building0NN
provision0NN
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contribute0.0554128417527413VB
energy0.133960988253756NN
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conservation0NN
cultural0JJ
heritage0NN

Label: 17, with a score of: 0.270544893545785
WordScorePoS
adaptive0JJ
reuse0NN
building0.149940347231585NN
provision0NN
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contribute0.0249900578719309VB
energy0.0109843026967392NN
resource0.0375923735942499NN
conservation0NN
cultural0JJ
heritage0NN

Label: 11, with a score of: 0.35985733535544
WordScorePoS
adaptive0JJ
reuse0NN
building0.0747035830159326NN
provision0NN
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energy0.0492535935240667NN
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cultural0.055245183797332JJ
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Transportation BOX Transport now contributes percent of global greenhouse gas emissions Metz

Label: 13, with a score of: 0.549600257493144
WordScorePoS
building0NN
sustainability0NN
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transportation0NN
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global0.0375696197697663JJ
greenhouse0.0671298309154199NN
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emission0.19598742448524NN

Label: 17, with a score of: 0.386577552842019
WordScorePoS
building0.149940347231585NN
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transportation0NN
transport0.0249900578719309NN
contribute0.0249900578719309VB
global0.0201198688839505JJ
greenhouse0NN
gas0NN
emission0NN

Label: 12, with a score of: 0.453057306142119
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
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contribute0.0554128417527413VB
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Label: 11, with a score of: 0.35937276288611
WordScorePoS
building0.0747035830159326NN
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emission0.0373517915079663NN

Label: 7, with a score of: 0.355500709815927
WordScorePoS
building0NN
sustainability0NN
world0.00261936653839306NN
transportation0NN
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et al

Label: 0, with a score of: 1

and these emissions are likely to increase exponentially in developing countries where demand for private transportation is rapidly increasing Box

Label: 13, with a score of: 0.669072228602743
WordScorePoS
emission0.19598742448524NN
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rapidly0RB

This demand has to be managed carefully from the outset because transport sector investments are resource intensive and very difficult to undo

Label: 17, with a score of: 0.444058987306008
WordScorePoS
demand0NN
manage0VB
transport0.0249900578719309NN
sector0.0459744293658165NN
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Label: 9, with a score of: 0.417878051492352
WordScorePoS
demand0.030745604615229NN
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sector0.0565629593551219NN
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intensive0JJ

Label: 8, with a score of: 0.343506521307032
WordScorePoS
demand0NN
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transport0NN
sector0.0252035516550537NN
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intensive0.0804764811724088JJ

Label: 2, with a score of: 0.316506672154236
WordScorePoS
demand0.0297950283197006NN
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transport0NN
sector0.0182713919265625NN
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Once road and rail networks are established and entire communities grow around them retroactively replanning employment and population centers is an almost impossible undertaking The Urgent Need for Sustainable Urban Transport The global vehicle fleet is set to increase from approximately million to between and billion by if we continue on businessÐasÐusual path with most of the growth taking place in developing countries UNEP

Label: 11, with a score of: 0.510962022478701
WordScorePoS
road0.04477822029736NN
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urgent0JJ
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global0.00501208088147356JJ
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Label: 8, with a score of: 0.49378193158325
WordScorePoS
road0NN
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grow0VB
employment0.173804667214358NN
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urgent0JJ
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Label: 12, with a score of: 0.380480985936742
WordScorePoS
road0NN
establish0VB
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employment0NN
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urgent0JJ
sustainable0.0162522423871414JJ
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transport0.0277064208763707NN
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Label: 10, with a score of: 0.350924577204601
WordScorePoS
road0NN
establish0VB
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employment0NN
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urgent0JJ
sustainable0.00138439773401932JJ
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global0.0133009891881297JJ
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With the middle class in developing countries becoming more affluent and more dependent on private vehicles cheaper vehicles such as the Tata Nano and Bajaj RE becoming available at cost of around and slow turnover of vehicle stock both the demand for transport fuels and associated emissions are expected to further increase in the coming years

Label: 12, with a score of: 0.887683746196914
WordScorePoS
middle0JJ
class0NN
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country0NN
private0JJ
vehicle0.217007869186068NN
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slow0JJ
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increase0.00958677785775002NN

Label: 13, with a score of: 0.27481838680944
WordScorePoS
middle0JJ
class0NN
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private0JJ
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The lack of progress towards increasing vehicle fuel efficiency and reducing the carbon content of fuel coupled with the increase in the number of vehicle miles traveled is already negating air pollution reductions resulting from the adoption of green transport systems such as bus rapid transit light rail and subways

Label: 12, with a score of: 0.73542042537739
WordScorePoS
lack0NN
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Label: 11, with a score of: 0.360974094209112
WordScorePoS
lack0.0229054731708393NN
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If not contained the increase in emissions from the transport sector will offset greenhouse gas reductions being made in other sectors

Label: 13, with a score of: 0.667173636680953
WordScorePoS
increase0.0145316075945746NN
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transport0NN
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greenhouse0.0671298309154199NN
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Label: 7, with a score of: 0.396113058141285
WordScorePoS
increase0.0162235147372888NN
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Label: 12, with a score of: 0.369135275702867
WordScorePoS
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There are three sources of concern Transport fuels are large emitters of pollutants

Label: 7, with a score of: 0.571874690793483
WordScorePoS
source0.0274787313211514NN
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Label: 11, with a score of: 0.417098531451052
WordScorePoS
source0NN
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Label: 6, with a score of: 0.342575100460855
WordScorePoS
source0.0613562156822703NN
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Label: 12, with a score of: 0.340148142695029
WordScorePoS
source0NN
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Road transportation vehicles emit harmful substances such as carbon dioxide carbon monoxide non methanous volatile organic compounds methane nitrogen oxides particulate matter and sulfur dioxide

Label: 12, with a score of: 0.631310618332486
WordScorePoS
road0NN
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substance0NN
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While gasoline fueled vehicles account for percent to percent of total volatile organic compounds and carbon monoxide diesel vehicles are the main source of nitrogen oxides sulfur dioxide and particulates Rodrigue

Label: 12, with a score of: 0.878498025762518
WordScorePoS
fuel0.0332151194779746NN
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They account for percent of nitrogen oxides percent sulfur dioxide and percent of particulate emissions

Label: 13, with a score of: 0.833417596861433
WordScorePoS
account0NN
dioxide0.0414107553022249NN
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Label: 12, with a score of: 0.397218973208711
WordScorePoS
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In many megacities in Asia particulate matter already exceeds micrograms per cubic meter which is above the WHO standard and levels are rising

Label: 13, with a score of: 0.860283483507835
WordScorePoS
asia0IN
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Road transport is major cause of urban smog which is mixture of smoke fog and chemical fumes

Label: 11, with a score of: 0.933764947303751
WordScorePoS
road0.04477822029736NN
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The current and expected growth in transport fuel demand will cause tremendous public health burden

Label: 3, with a score of: 0.436557383962017
WordScorePoS
current0JJ
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growth0NN
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Label: 8, with a score of: 0.389318979467955
WordScorePoS
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Label: 12, with a score of: 0.35503727673362
WordScorePoS
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Label: 5, with a score of: 0.346622648342123
WordScorePoS
current0JJ
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Label: 10, with a score of: 0.31643336612104
WordScorePoS
current0JJ
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Particulate pollution has been found to increase chronic cough in children

Label: 6, with a score of: 0.576120517774237
WordScorePoS
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Label: 3, with a score of: 0.493637789647632
WordScorePoS
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Label: 4, with a score of: 0.322629795690065
WordScorePoS
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Prospective cohort studies conducted by the American Cancer Society found that long term exposure to particulate matter is associated with respiratory illness in children

Label: 17, with a score of: 0.448856013552531
WordScorePoS
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Label: 16, with a score of: 0.432339903949717
WordScorePoS
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Label: 1, with a score of: 0.39943820086183
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Label: 10, with a score of: 0.368051470905118
WordScorePoS
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Label: 3, with a score of: 0.356549556192439
WordScorePoS
study0NN
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Epidemiological and clinical investigations suggest strong link between particulate pollution and cardiopulmonary morbidity and mortality

Label: 3, with a score of: 0.860161426850241
WordScorePoS
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Label: 9, with a score of: 0.308594532788973
WordScorePoS
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According to the Clean Air Task Force diesel exhaust poses cancer risk

Label: 7, with a score of: 0.492625549932742
WordScorePoS
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Label: 3, with a score of: 0.523267262843146
WordScorePoS
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Label: 11, with a score of: 0.396105668723575
WordScorePoS
clean0JJ
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times higher than the combined total cancer risk from all other air toxins

Label: 12, with a score of: 0.507030007899953
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Label: 3, with a score of: 0.441925510555556
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Label: 11, with a score of: 0.427381934587663
WordScorePoS
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Growing transport fuel demand in developing countries particularly for diesel will accelerate global climate change

Label: 13, with a score of: 0.842877641896939
WordScorePoS
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Label: 7, with a score of: 0.36100048620663
WordScorePoS
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The transport sector accounts for percent of all greenhouse gas emissions and percent of all energy related carbon dioxide emissions globally

Label: 7, with a score of: 0.695436361336859
WordScorePoS
transport0NN
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globally0RB

Label: 13, with a score of: 0.545608860353327
WordScorePoS
transport0NN
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Label: 12, with a score of: 0.352696113136478
WordScorePoS
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The troubling aspect of transport sector emissions is that they are increasing quite rapidly and will continue to do so in the years and decades to come

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WordScorePoS
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WordScorePoS
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Carbon dioxide emissions from the transport sector increased by percent between and

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WordScorePoS
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WordScorePoS
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They are expected to rise by another percent in the period and by they will be up percent from levels

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WordScorePoS
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Black carbon along with methane is one of the major global warming agents after carbon dioxide

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WordScorePoS
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Increased use of diesel fuel in the transport sector will lead to further increases in particulate emissions including black carbon as vehicles tend to be poorly maintained in developing countries

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Label: 11, with a score of: 0.325154035193502
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The discussion on urban transport goes hand in hand with city density and land use

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WordScorePoS
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As discussed in Chapter there is empirical evidence that denser cities emit less greenhouse gas have greater satisfaction and well being and provide transportation services at much lower cost than sprawling cities

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WordScorePoS
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In many cases transport mode choice management combined with and land use management has led to cost efficient use of valuable urban land and infrastructure

Label: 11, with a score of: 0.696695801069328
WordScorePoS
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Label: 15, with a score of: 0.429638488640214
WordScorePoS
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Portland Oregon has saved billion annually through three decades of coordinated policies to change land use and transport systems

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Measures include modest increases in building density light rail transit schemes and policies to encourage walking and cycling

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Other cities like Copenhagen Hong Kong London New York Paris Singapore Vienna and Zurich show that it is possible to foster green growth decoupling economic goods from environmental bads

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WordScorePoS
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There is wide consensus that the policy package to reduce greenhouse gas emissions from transport should contain three types of policies reductions in private motorized transportation and increases in public and non motorized modes of transport increases in the fuel efficiency of vehicles and the use of alternative fuels and promotion of density and efficient public transportation

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Label: 11, with a score of: 0.399262271851087
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These actions have concrete and measurable co benefits

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Annex summarizes the policies that cities around the world have adopted to increase their efficiency and reduce the negative environmental impact of emissions

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To increase ridership on public transit cities have improved the existing public transportation network subsidized the cost of transit for examples from China see Hu et al

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improved the safety of the system and made efforts to better communicate service times and delays to Congestion Pricing BOX World Bank

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WordScorePoS
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A lower emission trajectory can be achieved if government backed alternatives for low carbon development gain traction

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WordScorePoS
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Policy tools such as congestion pricing have been successful in reducing demand for private transportation

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For example London congestion charge has reduced congestion by an estimated percent between February and February in comparison to the same period in previous years Transport for London and carbon dioxide emissions from traffic inside the charging zone were cut by

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percent Carslaw

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Congestion costs are even higher in rapidly growing developing country cities

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The costs of congestion are estimated at

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percent of local GDP in Buenos Aires

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percent in Mexico City and

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percent in Dakar World Bank

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Mexico City and Bogotá have introduced number plate restrictions with measurable impacts on congestion and air quality Mahendra

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customers

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Cities have also facilitated biking for example by replacing lanes for vehicle traffic with protected bike only lanes and by installing clear signage of bicycle routes and rights of way as in Montreal Paris and Rio de Janeiro

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Local authorities can also pursue alternative modes of transport to manage freight movement

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Some European port cities provide incentives for alternatives to truck transport Antwerp provides subsidies for inland river transport while Rotterdam has dedicated freight railways

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To reduce emissions from urban transport the alternatives include reducing emissions per kilogram of fuel combusted by replacing the usual fossil fuels with less polluting fuels for example natural gas or electricity reducing the fuel combusted per kilometer traveled by promoting greater vehicle efficiency and reducing the vehicle kilometers traveled through measures such as congestion charges Box vehicle registration quotas and replacement of private motor vehicles by public transportation

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Complementary measures including fiscal policy fuel tax vehicle tax congestion and parking charges regulatory policy fuel standards emission standards and vehicle inspection urban planning high density and transit oriented development and temporary subsidies and tax breaks can also be used to contain rising emissions from urban transport in developing countries

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develop0VB
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Label: 12, with a score of: 0.507294077225914
WordScorePoS
building0NN
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urban0JJ
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Label: 13, with a score of: 0.355047386992749
WordScorePoS
building0NN
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urban0JJ
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Label: 17, with a score of: 0.287107863613917
WordScorePoS
building0.149940347231585NN
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Label: 14, with a score of: 0.103201745775434
WordScorePoS
building0NN
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In addition many countries promote the purchase of ultra low carbon vehicles through subsidies

Label: 12, with a score of: 0.778173517553647
WordScorePoS
addition0NN
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carbon0NN
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Label: 14, with a score of: 0.49568460026078
WordScorePoS
addition0NN
country0NN
promote0VB
carbon0.0271683202880942NN
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In Belgium Canada China the Netherlands the United Kingdom and the United States those subsidies can be as large as per vehicle Perkins

Label: 12, with a score of: 0.754734399202243
WordScorePoS
unite0VB
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Label: 14, with a score of: 0.433634761002771
WordScorePoS
unite0VB
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While developing countries may not be able to afford such high consumer incentives they can reduce urban transport emissions by speeding up the turnover of the vehicle stock

Label: 12, with a score of: 0.821578680998698
WordScorePoS
develop0VB
country0NN
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Label: 11, with a score of: 0.486954191614385
WordScorePoS
develop0VB
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Label: 13, with a score of: 0.238205513832558
WordScorePoS
develop0VB
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reduce0.00375696197697663VB
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emission0.19598742448524NN
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Temporary subsidies and tax breaks may encourage people to trade in their old higher polluting vehicles and promote environmentally friendly transportation choices like compressed natural gas CNG

Label: 12, with a score of: 0.806219167255214
WordScorePoS
subsidy0.0664302389559493NN
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Label: 14, with a score of: 0.3418205849812
WordScorePoS
subsidy0.162850158165781NN
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The introduction of cleaner transport fuel has major air quality benefits

Label: 7, with a score of: 0.603559955862105
WordScorePoS
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Label: 12, with a score of: 0.412445994268891
WordScorePoS
introduction0NN
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Label: 11, with a score of: 0.379540681967412
WordScorePoS
introduction0NN
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In response to extreme deterioration of air quality in Delhi the Supreme Court of India passed an order in to reduce vehicular pollution

Label: 1, with a score of: 0.740750721505869
WordScorePoS
response0NN
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It called for replacement of all pre automobiles and taxis with new vehicles using clean fuels conversion of buses older than eight years to CNG or other clean fuels and steady conversion of the entire city bus fleet to single clean fuel mode by March

Label: 11, with a score of: 0.643659298643274
WordScorePoS
vehicle0NN
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Label: 7, with a score of: 0.635436231035638
WordScorePoS
vehicle0NN
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Label: 12, with a score of: 0.335392123214871
WordScorePoS
vehicle0.217007869186068NN
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Since Delhi has become the city with highest fraction of CNG run public vehicles in the world

Label: 11, with a score of: 0.925105617896717
WordScorePoS
city0.607697021770652NN
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Label: 12, with a score of: 0.344704902831427
WordScorePoS
city0NN
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The impact of CNG conversion has led to very large improvement in air quality

Label: 12, with a score of: 0.578093148335437
WordScorePoS
impact0.0866018859977851NN
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Label: 7, with a score of: 0.421662666453482
WordScorePoS
impact0NN
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Label: 9, with a score of: 0.331704819659092
WordScorePoS
impact0.0160169090870187NN
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air0NN
quality0.022550656390892NN

Label: 4, with a score of: 0.328341353024145
WordScorePoS
impact0NN
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percent reduction in emissions of sulfur dioxide

Label: 13, with a score of: 0.899592073847233
WordScorePoS
reduction0.0403047273067529NN
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dioxide0.0414107553022249NN

percent in small particulate matter and percent in PM Chelani Asha and Devotta

Label: 0, with a score of: 1

Green concerns are present in many large cities transportation plans

Label: 11, with a score of: 0.985761289578146
WordScorePoS
green0.04477822029736JJ
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In recent years older more established cities such as Amsterdam Copenhagen London and New York have invested in pro cycling and walking strategies while younger cities such as Bogotá Parana and São Paulo have invested in bus rapid transit solutions with well established success

Label: 11, with a score of: 0.972890568047123
WordScorePoS
establish0VB
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The plan prepared by Beijing for the Olympics focused on public transportation metro and light rail stringent vehicle emission limits encouraging the use of automobiles powered by cleaner fuels converting percent of public buses and percent of taxis to cleaner energy and pricing downtown parking lots

Label: 7, with a score of: 0.671955593849103
WordScorePoS
plan0NN
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Label: 12, with a score of: 0.486770311083087
WordScorePoS
plan0.0144336476662975NN
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Label: 13, with a score of: 0.314056003487051
WordScorePoS
plan0.0291713033871158NN
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In Hong Kong to reduce vehicle emissions the city is expanding and upgrading the public transport infrastructure with an emphasis on railways and using tax incentive schemes for environment friendly private cars and commercial vehicles

Label: 11, with a score of: 0.672886725303513
WordScorePoS
reduce0.0200483235258943VB
vehicle0NN
emission0.0373517915079663NN
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private0JJ
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Label: 12, with a score of: 0.58529504423875
WordScorePoS
reduce0.022306853314744VB
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Label: 9, with a score of: 0.260781756346247
WordScorePoS
reduce0VB
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Label: 13, with a score of: 0.163860865302951
WordScorePoS
reduce0.00375696197697663VB
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This tax incentive system includes percent reduction in the first registration tax FRT for vehicles that have at least percent higher than average fuel efficiency in the corresponding private car class

Label: 12, with a score of: 0.54444385993611
WordScorePoS
tax0NN
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Label: 13, with a score of: 0.345625792387766
WordScorePoS
tax0NN
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Label: 10, with a score of: 0.325865616424739
WordScorePoS
tax0NN
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Label: 16, with a score of: 0.452913355972196
WordScorePoS
tax0.0811931365363091NN
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Label: 17, with a score of: 0.37239182302064
WordScorePoS
tax0.036961556179878NN
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In the case of Singapore the city energy program is committed to increasing the share of public transport modes from percent to over percent in the next years promoting less polluting cars with green vehicle rebate raising consumer awareness with fuel economy labeling scheme and managing road congestion through infrastructure development refinement of car ownership and usage restraint measures such as an electronic road pricing system

Label: 12, with a score of: 0.603110629334213
WordScorePoS
city0NN
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Label: 11, with a score of: 0.539836776150169
WordScorePoS
city0.607697021770652NN
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Label: 7, with a score of: 0.386477085958176
WordScorePoS
city0NN
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Label: 9, with a score of: 0.253454085153889
WordScorePoS
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Water Sustainable cities face two main issues regarding the water sector Miller and Yates

Label: 6, with a score of: 0.800853172279987
WordScorePoS
water0.556409482163189NN
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Label: 11, with a score of: 0.498659593269483
WordScorePoS
water0.0458109463416786NN
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Label: 12, with a score of: 0.287030981818445
WordScorePoS
water0.186896403054502NN
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First they need to prepare for potential disruptions in water supply associated with global climate change

Label: 13, with a score of: 0.678581343863131
WordScorePoS
potential0NN
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Label: 6, with a score of: 0.579196647275986
WordScorePoS
potential0NN
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Label: 12, with a score of: 0.259100740187769
WordScorePoS
potential0NN
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A recent study indicates that percent of cities foresee substantive risks to their water supply in the future CDP the two most common risks are increased water stress or scarcity and declining water quality

Label: 6, with a score of: 0.828420127898115
WordScorePoS
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Label: 11, with a score of: 0.411969112241984
WordScorePoS
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Label: 12, with a score of: 0.311755692659186
WordScorePoS
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The next chapter looks at addressing these risks in the context of climate http www

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epd

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gov

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hk epd english climate change transport

Label: 13, with a score of: 0.919625394623237
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html PART II THE PATH TO SUSTAINABILITY change adaptation

Label: 13, with a score of: 0.93001907820259
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Second the optimal approach to making the water sector more efficient and less greenhouse gas intensive needs to be determined

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WordScorePoS
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Label: 12, with a score of: 0.450965179512882
WordScorePoS
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Water provision is carbon intensive because of the energy used to extract water treat it pump and distribute it and collect and treat wastewater

Label: 6, with a score of: 0.853789248625231
WordScorePoS
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Label: 7, with a score of: 0.35680319844653
WordScorePoS
water0NN
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Label: 12, with a score of: 0.325179287894356
WordScorePoS
water0.186896403054502NN
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In many developing countries water utilities bear the cost of energy losses caused by inefficient distribution networks losses in the underground water network and inefficient pumping stations

Label: 6, with a score of: 0.778991931212995
WordScorePoS
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Label: 12, with a score of: 0.434458064085033
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Label: 7, with a score of: 0.341532634074939
WordScorePoS
develop0VB
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Annually about half of the clean water is lost

Label: 6, with a score of: 0.909769536752972
WordScorePoS
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Label: 12, with a score of: 0.287553980627342
WordScorePoS
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Label: 7, with a score of: 0.192371124502978
WordScorePoS
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To save water greenhouse gas emissions and money water authorities can improve water pumping installations which are generally over designed and hence consume more energy

Label: 6, with a score of: 0.813346278738952
WordScorePoS
save0IN
water0.556409482163189NN
greenhouse0NN
gas0NN
emission0NN
money0NN
improve0.0511916925627245VB
consume0VB
energy0.0153390539205676NN

Label: 12, with a score of: 0.40179654367871
WordScorePoS
save0IN
water0.186896403054502NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
emission0.0554128417527413NN
money0NN
improve0VB
consume0.0409791940864438VB
energy0.133960988253756NN

Label: 7, with a score of: 0.342042284049474
WordScorePoS
save0IN
water0NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN
money0NN
improve0.0229264916384433VB
consume0VB
energy0.494617163780726NN

Label: 13, with a score of: 0.165325905436725
WordScorePoS
save0IN
water0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
money0NN
improve0.0102677744436108VB
consume0VB
energy0.0123065238088614NN

They can also use renewable energy such as solar or wind power for pumping although this may incur initial higher costs

Label: 7, with a score of: 0.910088741593573
WordScorePoS
renewable0.158624482267508JJ
energy0.494617163780726NN
power0NN
incur0VB
cost0NN

Label: 12, with a score of: 0.257880514750415
WordScorePoS
renewable0.0234335422829798JJ
energy0.133960988253756NN
power0NN
incur0VB
cost0.0277064208763707NN

In addition integrated water management involving rainwater harvesting separation of wastewater by source and water efficient fixtures can lead to energy savings and greenhouse gas reductions

Label: 6, with a score of: 0.777957040524337
WordScorePoS
addition0NN
integrate0.0251182378961834VB
water0.556409482163189NN
management0.0428007293971684NN
involve0VB
rainwater0NN
harvest0.041835863322702NN
wastewater0.204998018174509NN
source0.0613562156822703NN
lead0NN
energy0.0153390539205676NN
greenhouse0NN
gas0NN
reduction0NN

Label: 12, with a score of: 0.420678549716746
WordScorePoS
addition0NN
integrate0.0199423462679015VB
water0.186896403054502NN
management0.0339811641917276NN
involve0.0819583881728876VB
rainwater0NN
harvest0.0664302389559493NN
wastewater0NN
source0NN
lead0.0169905820958638NN
energy0.133960988253756NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
reduction0.0199423462679015NN

Label: 7, with a score of: 0.367356238709165
WordScorePoS
addition0NN
integrate0VB
water0NN
management0NN
involve0VB
rainwater0NN
harvest0NN
wastewater0NN
source0.0274787313211514NN
lead0.0383371018038178NN
energy0.494617163780726NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
reduction0NN

Many cities are currently planning for and financing investments in water infrastructure retrofits which can be cost effective when financing for new infrastructure is hard to obtain

Label: 11, with a score of: 0.65901597887497
WordScorePoS
city0.607697021770652NN
plan0.0583751976558017NN
investment0NN
water0.0458109463416786NN
infrastructure0.0194583992186006NN
cost0NN
obtain0VB

Label: 6, with a score of: 0.520304090127157
WordScorePoS
city0NN
plan0NN
investment0NN
water0.556409482163189NN
infrastructure0.0181797964452808NN
cost0NN
obtain0VB

Label: 9, with a score of: 0.403610526074239
WordScorePoS
city0NN
plan0NN
investment0.0377086395700813NN
water0.0377086395700813NN
infrastructure0.192202909044225NN
cost0NN
obtain0VB

Label: 12, with a score of: 0.251713800372743
WordScorePoS
city0NN
plan0.0144336476662975NN
investment0NN
water0.186896403054502NN
infrastructure0.028867295332595NN
cost0.0277064208763707NN
obtain0VB

Cities also need to promote more efficient water management through smart water policies and better information for consumers

Label: 6, with a score of: 0.703575005556223
WordScorePoS
city0NN
promote0VB
water0.556409482163189NN
management0.0428007293971684NN
policy0NN
information0NN
consumer0NN

Label: 12, with a score of: 0.490115636003624
WordScorePoS
city0NN
promote0.0148712355431627VB
water0.186896403054502NN
management0.0339811641917276NN
policy0.0203215381458133NN
information0.0365348149782971NN
consumer0.325511803779102NN

Label: 11, with a score of: 0.475942083104021
WordScorePoS
city0.607697021770652NN
promote0VB
water0.0458109463416786NN
management0.0687164195125178NN
policy0.0136980135278128NN
information0NN
consumer0NN

As in the case of energy decentralized systems may be an alternative to centralized piped water supply systems particularly in fast growing cities in Africa and Asia

Label: 11, with a score of: 0.621558444828036
WordScorePoS
energy0.0492535935240667NN
system0.0315914058171786NN
alternative0NN
water0.0458109463416786NN
supply0NN
grow0.0229054731708393VB
city0.607697021770652NN
africa0IN
asia0IN

Label: 6, with a score of: 0.497010184664217
WordScorePoS
energy0.0153390539205676NN
system0NN
alternative0NN
water0.556409482163189NN
supply0.0590310971343993NN
grow0VB
city0NN
africa0IN
asia0IN

Label: 7, with a score of: 0.431385354441188
WordScorePoS
energy0.494617163780726NN
system0NN
alternative0NN
water0NN
supply0.0528748274225028NN
grow0VB
city0NN
africa0IN
asia0IN

Label: 12, with a score of: 0.334979908037322
WordScorePoS
energy0.133960988253756NN
system0NN
alternative0NN
water0.186896403054502NN
supply0.0703006268489394NN
grow0.0339811641917276VB
city0NN
africa0IN
asia0IN

Water conservation can be encouraged by proper pricing efficiency standards for appliances incentives to use low flow appliances education and information outreach and focused efforts to promote conservation OECD

Label: 6, with a score of: 0.632650973643705
WordScorePoS
water0.556409482163189NN
conservation0NN
encouraged0JJ
proper0JJ
standard0NN
incentive0NN
education0NN
information0NN
focus0NN
promote0VB
oecd0NN

Label: 12, with a score of: 0.452474579777199
WordScorePoS
water0.186896403054502NN
conservation0NN
encouraged0JJ
proper0JJ
standard0.0409791940864438NN
incentive0NN
education0.0101607690729067NN
information0.0365348149782971NN
focus0NN
promote0.0148712355431627VB
oecd0.108503934593034NN

Label: 4, with a score of: 0.311217637753274
WordScorePoS
water0NN
conservation0NN
encouraged0JJ
proper0JJ
standard0NN
incentive0NN
education0.244244978014045NN
information0.0182963616752599NN
focus0NN
promote0.0111711050494158VB
oecd0NN

Moreover cities could pass regulations to increase the use of recycled water

Label: 6, with a score of: 0.675678235902425
WordScorePoS
city0NN
regulation0NN
increase0.00905621749519576NN
recycle0.103230094596636VB
water0.556409482163189NN

Label: 11, with a score of: 0.663596608558915
WordScorePoS
city0.607697021770652NN
regulation0NN
increase0.00323105002784081NN
recycle0VB
water0.0458109463416786NN

Label: 12, with a score of: 0.281349022706131
WordScorePoS
city0NN
regulation0NN
increase0.00958677785775002NN
recycle0.0819583881728876VB
water0.186896403054502NN

For example in Melbourne households are required to use recycled water metered and delivered separately rather than potable water for toilet flushing washing cars and landscape watering

Label: 6, with a score of: 0.901521234981463
WordScorePoS
household0NN
require0VB
recycle0.103230094596636VB
water0.556409482163189NN
deliver0VB
toilet0.0683326727248364NN
car0NN

Label: 12, with a score of: 0.407662428827426
WordScorePoS
household0.0554128417527413NN
require0.0468670845659596VB
recycle0.0819583881728876VB
water0.186896403054502NN
deliver0.0332151194779746VB
toilet0NN
car0.0542519672965169NN

A study of cities in Canada and the United States found that watering restrictions on outdoor watering could be replaced by drought pricing leading to equivalent water savings Mansur

Label: 6, with a score of: 0.851127458840986
WordScorePoS
study0NN
city0NN
unite0VB
water0.556409482163189NN
restriction0NN
drought0.041835863322702NN
lead0NN
equivalent0JJ

Label: 11, with a score of: 0.370646784746012
WordScorePoS
study0NN
city0.607697021770652NN
unite0VB
water0.0458109463416786NN
restriction0NN
drought0NN
lead0NN
equivalent0JJ

Label: 12, with a score of: 0.32039691909143
WordScorePoS
study0NN
city0NN
unite0VB
water0.186896403054502NN
restriction0NN
drought0NN
lead0.0169905820958638NN
equivalent0.0664302389559493JJ

Toronto s WaterSaver program provides cash incentives for industrial and institutional facilities to reduce water consumption

Label: 6, with a score of: 0.622247819141578
WordScorePoS
incentive0NN
industrial0JJ
institutional0JJ
facility0NN
reduce0.00936547854413134VB
water0.556409482163189NN
consumption0NN

Label: 12, with a score of: 0.595389775095234
WordScorePoS
incentive0NN
industrial0JJ
institutional0JJ
facility0NN
reduce0.022306853314744VB
water0.186896403054502NN
consumption0.332151194779746NN

Label: 9, with a score of: 0.391565768653016
WordScorePoS
incentive0NN
industrial0.318320101004075JJ
institutional0JJ
facility0NN
reduce0VB
water0.0377086395700813NN
consumption0NN

Label: 8, with a score of: 0.168631598245723
WordScorePoS
incentive0NN
industrial0JJ
institutional0JJ
facility0NN
reduce0.0055149369084567VB
water0NN
consumption0.147812295323721NN

The reductions in consumption are high enough to enable the city to buy back water or sewer capacity OECD

Label: 11, with a score of: 0.672198236854035
WordScorePoS
reduction0.0268848280079943NN
consumption0.08955644059472NN
enable0.0373517915079663VB
city0.607697021770652NN
water0.0458109463416786NN
capacity0.0112376090361851NN
oecd0NN

Label: 12, with a score of: 0.538578726958964
WordScorePoS
reduction0.0199423462679015NN
consumption0.332151194779746NN
enable0VB
city0NN
water0.186896403054502NN
capacity0.00833571598658786NN
oecd0.108503934593034NN

Label: 6, with a score of: 0.465555311666117
WordScorePoS
reduction0NN
consumption0NN
enable0VB
city0NN
water0.556409482163189NN
capacity0.0104991907358033NN
oecd0NN

Label: 8, with a score of: 0.131540448003143
WordScorePoS
reduction0NN
consumption0.147812295323721NN
enable0VB
city0NN
water0NN
capacity0.0123650647908625NN
oecd0NN

Solid Waste Management Effective management of municipal solid waste MSW poses one of the biggest challenges of the urban world UN Habitat

Label: 11, with a score of: 0.802666992189307
WordScorePoS
waste0.0373517915079663NN
management0.0687164195125178NN
municipal0.0731385760866977JJ
pose0VB
challenge0.0947742174515357NN
urban0.27622591898666JJ
world0.00626002771693937NN
habitat0NN

Label: 12, with a score of: 0.423628225651743
WordScorePoS
waste0.138532104381853NN
management0.0339811641917276NN
municipal0JJ
pose0VB
challenge0NN
urban0JJ
world0.00464349782489754NN
habitat0NN

Global waste production is increasing currently the world cities produce

Label: 11, with a score of: 0.831724200422234
WordScorePoS
global0.00501208088147356JJ
waste0.0373517915079663NN
production0NN
increase0.00323105002784081NN
world0.00626002771693937NN
city0.607697021770652NN
produce0VB

Label: 12, with a score of: 0.412636905948969
WordScorePoS
global0.022306853314744JJ
waste0.138532104381853NN
production0.118934074671047NN
increase0.00958677785775002NN
world0.00464349782489754NN
city0NN
produce0.0332151194779746VB

billion tonnes of waste annually and they will produce

Label: 12, with a score of: 0.922061224260779
WordScorePoS
tonne0.108503934593034NN
waste0.138532104381853NN
annually0RB
produce0.0332151194779746VB

billion tonnes by Hoornweg and Bhada Tata

Label: 12, with a score of: 1
WordScorePoS
tonne0.108503934593034NN

This waste directly affects environmental and public health at local and global scales and the task of managing it in socially environmentally and economically acceptable manner falls almost entirely to cities

Label: 11, with a score of: 0.790880275472817
WordScorePoS
waste0.0373517915079663NN
environmental0.0537696560159885JJ
public0.0583751976558017NN
health0.0229054731708393NN
local0.0194583992186006JJ
global0.00501208088147356JJ
scale0NN
manage0VB
socially0RB
environmentally0RB
economically0RB
manner0NN
fall0NN
city0.607697021770652NN

Label: 12, with a score of: 0.346207565092338
WordScorePoS
waste0.138532104381853NN
environmental0.0598270388037045JJ
public0.028867295332595NN
health0.0339811641917276NN
local0.0144336476662975JJ
global0.022306853314744JJ
scale0NN
manage0VB
socially0RB
environmentally0RB
economically0RB
manner0.0542519672965169NN
fall0NN
city0NN

Label: 3, with a score of: 0.344162568372345
WordScorePoS
waste0NN
environmental0JJ
public0.021678073222951NN
health0.191387734041787NN
local0JJ
global0.013959557403375JJ
scale0NN
manage0VB
socially0RB
environmentally0RB
economically0RB
manner0NN
fall0.123094312756122NN
city0NN

On local scale improper waste management especially open dumping and open burning pollutes water bodies contaminates air and land and attracts disease vectors

Label: 6, with a score of: 0.771120066698026
WordScorePoS
local0.0181797964452808JJ
scale0NN
waste0NN
management0.0428007293971684NN
dump0.0683326727248364NN
pollute0VB
water0.556409482163189NN
contaminate0.0683326727248364VB
air0NN
land0NN
attract0VB
disease0.103230094596636NN

Label: 12, with a score of: 0.447893868257109
WordScorePoS
local0.0144336476662975JJ
scale0NN
waste0.138532104381853NN
management0.0339811641917276NN
dump0NN
pollute0.0542519672965169VB
water0.186896403054502NN
contaminate0VB
air0.0554128417527413NN
land0.0144336476662975NN
attract0VB
disease0NN

Label: 3, with a score of: 0.258385900803212
WordScorePoS
local0JJ
scale0NN
waste0NN
management0.0127591822694525NN
dump0NN
pollute0VB
water0.0382775468083574NN
contaminate0VB
air0.020806307400253NN
land0NN
attract0VB
disease0.215415047323213NN

People who live near or work with solid waste have increased disease burdens Giusti

Label: 3, with a score of: 0.634782902513356
WordScorePoS
people0.00539943740774902NNS
live0.0300513417930436VB
waste0NN
increase0.0143984997539974NN
disease0.215415047323213NN
burden0NN

Label: 12, with a score of: 0.383128175408876
WordScorePoS
people0.0119834723221875NNS
live0VB
waste0.138532104381853NN
increase0.00958677785775002NN
disease0NN
burden0NN

Label: 6, with a score of: 0.386803156361248
WordScorePoS
people0.024149913320522NNS
live0.0252018374089947VB
waste0NN
increase0.00905621749519576NN
disease0.103230094596636NN
burden0NN

Label: 2, with a score of: 0.303599036723828
WordScorePoS
people0.0257736572183839NNS
live0.0143446978608332VB
waste0.0297950283197006NN
increase0.0128868286091919NN
disease0NN
burden0.0440683498519077NN

On global scale solid waste currently contributes to climate change emitting percent of total greenhouse gases though waste has the potential to be net sink of greenhouse gases if it is used as resource through recycling and reuse Bogner et al

Label: 13, with a score of: 0.658221388834773
WordScorePoS
global0.0375696197697663JJ
scale0NN
waste0NN
contribute0VB
climate0.291881734282273NN
change0.402565546215053NN
total0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
potential0NN
resource0NN
recycle0VB
reuse0NN

Label: 12, with a score of: 0.459934130918243
WordScorePoS
global0.022306853314744JJ
scale0NN
waste0.138532104381853NN
contribute0.0554128417527413VB
climate0NN
change0NN
total0.0468670845659596NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
potential0NN
resource0.0416785799329393NN
recycle0.0819583881728876VB
reuse0.0409791940864438NN

Label: 7, with a score of: 0.476044948955759
WordScorePoS
global0.0251662980592747JJ
scale0NN
waste0.0625160381065121NN
contribute0VB
climate0.115011305411453NN
change0.105749654845006NN
total0.0528748274225028NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
potential0NN
resource0NN
recycle0VB
reuse0NN

Some air pollutants emitted via waste incineration such as dioxins and furans mix globally and thus affect the Earth ecosystems and air quality

Label: 12, with a score of: 0.617397642107622
WordScorePoS
air0.0554128417527413NN
waste0.138532104381853NN
mix0NN
globally0RB
earth0NN
ecosystem0.0234335422829798NN
quality0.0203215381458133NN

Label: 15, with a score of: 0.349125240267397
WordScorePoS
air0NN
waste0NN
mix0NN
globally0RB
earth0.0316735178673487NN
ecosystem0.134075577362426NN
quality0NN

Label: 7, with a score of: 0.389524259012925
WordScorePoS
air0NN
waste0.0625160381065121NN
mix0.122412709674631NN
globally0RB
earth0NN
ecosystem0NN
quality0NN

Label: 14, with a score of: 0.36866386496617
WordScorePoS
air0.0271683202880942NN
waste0NN
mix0NN
globally0RB
earth0.0977100948994686NN
ecosystem0.0229784274580034NN
quality0NN

In any given city waste management can be described in terms of six components generation collection source handling transport treatment BUILDING SUSTAINABILITY IN AN URBANIZING WORLD and disposal Tchobanoglous and Krieth

Label: 11, with a score of: 0.801936426625034
WordScorePoS
city0.607697021770652NN
waste0.0373517915079663NN
management0.0687164195125178NN
term0NN
generation0NN
collection0NN
source0NN
transport0.0747035830159326NN
treatment0NN
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN

Label: 17, with a score of: 0.392735844309922
WordScorePoS
city0NN
waste0NN
management0NN
term0.149793392961411NN
generation0NN
collection0.048933054487825NN
source0.0109843026967392NN
transport0.0249900578719309NN
treatment0NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

Label: 12, with a score of: 0.302352856577176
WordScorePoS
city0NN
waste0.138532104381853NN
management0.0339811641917276NN
term0NN
generation0.0819583881728876NN
collection0NN
source0NN
transport0.0277064208763707NN
treatment0NN
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN

The chain begins with waste generation which varies with wealth culture climate demographics and time

Label: 12, with a score of: 0.638727948311831
WordScorePoS
chain0.0819583881728876NN
waste0.138532104381853NN
generation0.0819583881728876NN
culture0.0277064208763707NN
climate0NN
time0.0169905820958638NN

Label: 13, with a score of: 0.53704517334563
WordScorePoS
chain0NN
waste0NN
generation0NN
culture0NN
climate0.291881734282273NN
time0NN

Label: 7, with a score of: 0.326639839959953
WordScorePoS
chain0NN
waste0.0625160381065121NN
generation0NN
culture0NN
climate0.115011305411453NN
time0NN

As such waste quantity and composition are location and context specific

Label: 12, with a score of: 0.746101788611882
WordScorePoS
waste0.138532104381853NN
location0NN
context0NN

Label: 17, with a score of: 0.462608454099895
WordScorePoS
waste0NN
location0.048933054487825NN
context0.036961556179878NN

Label: 7, with a score of: 0.336696883775247
WordScorePoS
waste0.0625160381065121NN
location0NN
context0NN

Generally waste quantity and complexity increase with affluence and urbanization

Label: 11, with a score of: 0.727335826879576
WordScorePoS
waste0.0373517915079663NN
increase0.00323105002784081NN
urbanization0.146277152173395NN

Label: 12, with a score of: 0.576539512560813
WordScorePoS
waste0.138532104381853NN
increase0.00958677785775002NN
urbanization0NN

Label: 7, with a score of: 0.306486672930641
WordScorePoS
waste0.0625160381065121NN
increase0.0162235147372888NN
urbanization0NN

The amount of MSW per capita appears to be positively correlated with development as measured by the Human Development Index Figure as well as with urbanization Figure

Label: 11, with a score of: 0.513230240357144
WordScorePoS
amount0NN
capita0.04477822029736NN
positively0RB
development0.0100241617629471NN
measure0NN
human0.0273960270556257JJ
figure0NN
urbanization0.146277152173395NN

Label: 1, with a score of: 0.426685111773741
WordScorePoS
amount0.0614198493690006NN
capita0NN
positively0RB
development0.0164833460638534NN
measure0.103895385378356NN
human0JJ
figure0NN
urbanization0NN

But while citizens of all urbanized regions tend to produce more waste OECD residents are global outliers generating far more waste than those from any other region Figure

Label: 12, with a score of: 0.621613869862233
WordScorePoS
region0NN
produce0.0332151194779746VB
waste0.138532104381853NN
oecd0.108503934593034NN
global0.022306853314744JJ
generate0VB
figure0NN

Label: 1, with a score of: 0.519341905720914
WordScorePoS
region0.184259548107002NN
produce0VB
waste0NN
oecd0NN
global0JJ
generate0VB
figure0NN

Other variables such as wealth and culture contribute to this greater waste generation

Label: 12, with a score of: 0.844084247239991
WordScorePoS
culture0.0277064208763707NN
contribute0.0554128417527413VB
waste0.138532104381853NN
generation0.0819583881728876NN

Figure shows that the large expected increase in urban population particularly in Africa see Annex will bring commensurate increases in waste

Label: 11, with a score of: 0.781970513077928
WordScorePoS
figure0NN
expect0VB
increase0.00323105002784081NN
urban0.27622591898666JJ
population0.0194583992186006NN
africa0IN
bring0.0731385760866977VB
waste0.0373517915079663NN

Label: 12, with a score of: 0.389158485955128
WordScorePoS
figure0NN
expect0.0332151194779746VB
increase0.00958677785775002NN
urban0JJ
population0.0144336476662975NN
africa0IN
bring0VB
waste0.138532104381853NN

This figure does not show causality but does suggest future growth in waste production as the world urbanizes and develops

Label: 12, with a score of: 0.611627064081566
WordScorePoS
suggest0VB
future0.0277064208763707NN
growth0NN
waste0.138532104381853NN
production0.118934074671047NN
world0.00464349782489754NN
develop0VB

Label: 8, with a score of: 0.550169404716694
WordScorePoS
suggest0VB
future0NN
growth0.208565600657229NN
waste0NN
production0.0504071033101074NN
world0.00172202218602071NN
develop0VB

Label: 2, with a score of: 0.320966354889198
WordScorePoS
suggest0VB
future0.0297950283197006NN
growth0.0252000451111224NN
waste0.0297950283197006NN
production0.0548141757796873NN
world0.0124838525528648NN
develop0VB

Note that while rates of MSW generation per capita are already declining in OECD countries Sub Saharan Africa and South Asia are not projected to reach peak waste generation before

Label: 12, with a score of: 0.846018978814745
WordScorePoS
rate0NN
generation0.0819583881728876NN
capita0.0332151194779746NN
decline0.0234335422829798NN
oecd0.108503934593034NN
country0NN
saharan0JJ
africa0IN
south0RB
asia0IN
project0.0664302389559493NN
reach0.0332151194779746NN
waste0.138532104381853NN

This is problematic because these two regions will also have the highest total urban populations by followed by East Asia and the Pacific whose waste generation rates are projected to plateau only by

Label: 12, with a score of: 0.53595458432752
WordScorePoS
region0NN
total0.0468670845659596NN
urban0JJ
population0.0144336476662975NN
asia0IN
waste0.138532104381853NN
generation0.0819583881728876NN
rate0NN
project0.0664302389559493NN

Label: 11, with a score of: 0.512582503757343
WordScorePoS
region0NN
total0NN
urban0.27622591898666JJ
population0.0194583992186006NN
asia0IN
waste0.0373517915079663NN
generation0NN
rate0NN
project0NN

Label: 1, with a score of: 0.382090877995343
WordScorePoS
region0.184259548107002NN
total0NN
urban0JJ
population0NN
asia0IN
waste0NN
generation0NN
rate0.0639932865717261NN
project0NN

Latin America and the Caribbean the Middle East and North Africa and Eastern Europe and Central Asia are projected to reach peak waste by approximately but the impact of this earlier peak will be less significant because these are not the regions where urban population growth will be concentrated

Label: 11, with a score of: 0.47929611382593
WordScorePoS
middle0JJ
north0RB
africa0IN
eastern0JJ
central0JJ
asia0IN
project0NN
reach0NN
waste0.0373517915079663NN
approximately0RB
impact0.0194583992186006NN
region0NN
urban0.27622591898666JJ
population0.0194583992186006NN
growth0NN

Label: 12, with a score of: 0.461236889802132
WordScorePoS
middle0JJ
north0RB
africa0IN
eastern0JJ
central0JJ
asia0IN
project0.0664302389559493NN
reach0.0332151194779746NN
waste0.138532104381853NN
approximately0RB
impact0.0866018859977851NN
region0NN
urban0JJ
population0.0144336476662975NN
growth0NN

Label: 8, with a score of: 0.341817387143375
WordScorePoS
middle0JJ
north0RB
africa0IN
eastern0JJ
central0JJ
asia0IN
project0NN
reach0NN
waste0NN
approximately0RB
impact0NN
region0NN
urban0JJ
population0.0428212738652355NN
growth0.208565600657229NN

Label: 1, with a score of: 0.3211772373823
WordScorePoS
middle0JJ
north0RB
africa0IN
eastern0JJ
central0JJ
asia0IN
project0NN
reach0NN
waste0NN
approximately0RB
impact0NN
region0.184259548107002NN
urban0JJ
population0NN
growth0.0519476926891779NN

FIG

Label: 0, with a score of: 1

Waste Production Per Capita Tompkins County USA San Francisco USA

Label: 12, with a score of: 0.840587569785927
WordScorePoS
waste0.138532104381853NN
production0.118934074671047NN
capita0.0332151194779746NN

Rotterdam Netherlands Waste Produced kg cap day

Label: 1, with a score of: 0.764781408120925
WordScorePoS
waste0NN
produce0VB
day0.25973846344589NN

Label: 12, with a score of: 0.505697469530702
WordScorePoS
waste0.138532104381853NN
produce0.0332151194779746VB
day0NN

Cañete Peru

Label: 0, with a score of: 1

Adelaide Australia Vàrna Bulgaria Managua Nicaragua Sousse Tunisia

Label: 0, with a score of: 1

Moshi Tanzania Kunming China Curepipe Mauritius Delhi India Quezon City Philippines Bamako Mali Belo Horizonte Brazil Nairobi Kenya Lusaka Zambia Bengaluru India Ghorahi Nepal Dhaka Bangladesh

Label: 11, with a score of: 0.997380969185671
WordScorePoS
city0.607697021770652NN

Human Development Index Source Vergara and Tchobanoglous PART II THE PATH TO SUSTAINABILITY Municipal Solid Waste Generated Per Capita kg cap day

Label: 1, with a score of: 0.589935996334093
WordScorePoS
human0JJ
development0.0164833460638534NN
source0.0269969049497485NN
sustainability0NN
municipal0JJ
waste0NN
generate0VB
capita0NN
day0.25973846344589NN

Label: 12, with a score of: 0.477044402544285
WordScorePoS
human0.0101607690729067JJ
development0.022306853314744NN
source0NN
sustainability0.0409791940864438NN
municipal0JJ
waste0.138532104381853NN
generate0VB
capita0.0332151194779746NN
day0NN

Label: 11, with a score of: 0.374891258522072
WordScorePoS
human0.0273960270556257JJ
development0.0100241617629471NN
source0NN
sustainability0NN
municipal0.0731385760866977JJ
waste0.0373517915079663NN
generate0VB
capita0.04477822029736NN
day0NN

FIG

Label: 0, with a score of: 1

OECD Current Waste Production and Urbanization by Region

Label: 12, with a score of: 0.762831986872669
WordScorePoS
oecd0.108503934593034NN
current0.0332151194779746JJ
waste0.138532104381853NN
production0.118934074671047NN
urbanization0NN
region0NN

Label: 1, with a score of: 0.352114921894154
WordScorePoS
oecd0NN
current0JJ
waste0NN
production0NN
urbanization0NN
region0.184259548107002NN

Label: 11, with a score of: 0.350909854203705
WordScorePoS
oecd0NN
current0JJ
waste0.0373517915079663NN
production0NN
urbanization0.146277152173395NN
region0NN

ECA MENA LAC

Label: 0, with a score of: 1

EAP AFR

Label: 0, with a score of: 1

SAR Source Data from Hoornweg and Bhada Tata

Label: 17, with a score of: 0.644232611126071
WordScorePoS
source0.0109843026967392NN
datum0.0739231123597559NN

Label: 6, with a score of: 0.465538551744894
WordScorePoS
source0.0613562156822703NN
datum0NN

Percent Population Residing in Urban Areas Note The size of each circle corresponds to the total amount of waste generated in the region

Label: 11, with a score of: 0.58048517296413
WordScorePoS
population0.0194583992186006NN
urban0.27622591898666JJ
size0NN
total0NN
amount0NN
waste0.0373517915079663NN
generate0VB
region0NN

Label: 1, with a score of: 0.428221575313208
WordScorePoS
population0NN
urban0JJ
size0NN
total0NN
amount0.0614198493690006NN
waste0NN
generate0VB
region0.184259548107002NN

Label: 12, with a score of: 0.348310574567246
WordScorePoS
population0.0144336476662975NN
urban0JJ
size0NN
total0.0468670845659596NN
amount0NN
waste0.138532104381853NN
generate0VB
region0NN

Label: 10, with a score of: 0.304232358411355
WordScorePoS
population0.0860640399817412NN
urban0.0488696285210061JJ
size0.0396106020715138NN
total0NN
amount0NN
waste0NN
generate0VB
region0NN

AFR Africa EAP East Asia and Pacific ECA Europe and Central Asia LAC Latin America and the Caribbean MENA Middle East and North Africa SAR South Asia

Label: 0, with a score of: 1

FIG

Label: 0, with a score of: 1

Projected Waste Production and Urbanization by Region in Source Data from Hoornweg and Bhada Tata

Label: 12, with a score of: 0.624151161211237
WordScorePoS
project0.0664302389559493NN
waste0.138532104381853NN
production0.118934074671047NN
urbanization0NN
region0NN
source0NN
datum0NN

Label: 1, with a score of: 0.407092987626481
WordScorePoS
project0NN
waste0NN
production0NN
urbanization0NN
region0.184259548107002NN
source0.0269969049497485NN
datum0NN

Label: 11, with a score of: 0.353854541323095
WordScorePoS
project0NN
waste0.0373517915079663NN
production0NN
urbanization0.146277152173395NN
region0NN
source0NN
datum0NN

Note The size of each circle corresponds to the total amount of waste generated in the region

Label: 1, with a score of: 0.607296176502975
WordScorePoS
size0NN
total0NN
amount0.0614198493690006NN
waste0NN
generate0VB
region0.184259548107002NN

Label: 12, with a score of: 0.458289216480829
WordScorePoS
size0NN
total0.0468670845659596NN
amount0NN
waste0.138532104381853NN
generate0VB
region0NN

Label: 3, with a score of: 0.360018534024098
WordScorePoS
size0NN
total0NN
amount0.020806307400253NN
waste0NN
generate0VB
region0.124837844401518NN

Label: 2, with a score of: 0.318525874665147
WordScorePoS
size0NN
total0.0252000451111224NN
amount0NN
waste0.0297950283197006NN
generate0.0440683498519077VB
region0.0297950283197006NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD The composition of the waste that cities generate changes with demographics

Label: 11, with a score of: 0.911478027706685
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
waste0.0373517915079663NN
city0.607697021770652NN
generate0VB

Label: 17, with a score of: 0.239905635013638
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
waste0NN
city0NN
generate0VB

Label: 12, with a score of: 0.23119859242986
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
waste0.138532104381853NN
city0NN
generate0VB

Wealthier cities tend to produce more complex waste with higher proportion of electronics and plastics which are harder to manage

Label: 11, with a score of: 0.912447322985271
WordScorePoS
wealthier0JJR
city0.607697021770652NN
produce0VB
waste0.0373517915079663NN
proportion0NN
manage0VB

Label: 12, with a score of: 0.242943311289162
WordScorePoS
wealthier0JJR
city0NN
produce0.0332151194779746VB
waste0.138532104381853NN
proportion0NN
manage0VB

Electronic waste poses particular threat to developing cities since it contains toxic materials and is often exported from wealthier to poorer nations where it is informally managed Williams

Label: 11, with a score of: 0.889610475078318
WordScorePoS
waste0.0373517915079663NN
pose0VB
threat0NN
develop0VB
city0.607697021770652NN
material0.055245183797332NN
export0NN
wealthier0JJR
nation0NN
manage0VB

Label: 12, with a score of: 0.175982675429636
WordScorePoS
waste0.138532104381853NN
pose0VB
threat0NN
develop0VB
city0NN
material0NN
export0NN
wealthier0JJR
nation0NN
manage0VB

Lower income cities tend to have higher relative organic fraction in their waste this less complex waste is relatively easy to manage

Label: 11, with a score of: 0.851250442973177
WordScorePoS
income0NN
city0.607697021770652NN
relative0.0731385760866977JJ
waste0.0373517915079663NN
manage0VB

Label: 12, with a score of: 0.312162540888365
WordScorePoS
income0NN
city0NN
relative0JJ
waste0.138532104381853NN
manage0VB

Label: 10, with a score of: 0.262018062804394
WordScorePoS
income0.166475342664294NN
city0NN
relative0JJ
waste0NN
manage0.0660824365173543VB

However the global proliferation of disposable consumer products is increasing the complexity of waste everywhere

Label: 12, with a score of: 0.935060610440142
WordScorePoS
global0.022306853314744JJ
consumer0.325511803779102NN
product0.0199423462679015NN
increase0.00958677785775002NN
waste0.138532104381853NN

To deal with more difficult waste cities need to reduce waste production through market based instruments such as weight based waste fees often known as pay as you throw

Label: 11, with a score of: 0.704927172557857
WordScorePoS
waste0.0373517915079663NN
city0.607697021770652NN
reduce0.0200483235258943VB
production0NN
market0NN
base0NN
pay0.0315914058171786NN

Label: 12, with a score of: 0.560833607346707
WordScorePoS
waste0.138532104381853NN
city0NN
reduce0.022306853314744VB
production0.118934074671047NN
market0.0169905820958638NN
base0.0199423462679015NN
pay0NN

Another possibility borrowed from the energy sector is positive feedback mechanisms

Label: 7, with a score of: 0.831420543256972
WordScorePoS
energy0.494617163780726NN
sector0NN
positive0JJ
mechanism0NN

Label: 17, with a score of: 0.344264333910085
WordScorePoS
energy0.0109843026967392NN
sector0.0459744293658165NN
positive0JJ
mechanism0.147846224719512NN

Label: 12, with a score of: 0.25374015666228
WordScorePoS
energy0.133960988253756NN
sector0.0169905820958638NN
positive0JJ
mechanism0NN

In one study residents received their energy bills along with information about the average community consumption marked with smiley face if they used less energy than average

Label: 7, with a score of: 0.742014907377085
WordScorePoS
study0NN
receive0VB
energy0.494617163780726NN
information0NN
average0NN
community0NN
consumption0NN

Label: 12, with a score of: 0.490257836442061
WordScorePoS
study0NN
receive0VB
energy0.133960988253756NN
information0.0365348149782971NN
average0NN
community0.0169905820958638NN
consumption0.332151194779746NN

Label: 13, with a score of: 0.292713761155449
WordScorePoS
study0NN
receive0VB
energy0.0123065238088614NN
information0NN
average0.1656430212089NN
community0.0343390275626203NN
consumption0NN

Label: 10, with a score of: 0.235138137915557
WordScorePoS
study0NN
receive0VB
energy0NN
information0NN
average0.146608885563018NN
community0.0202620733250388NN
consumption0NN

Label: 8, with a score of: 0.110872544116209
WordScorePoS
study0NN
receive0VB
energy0NN
information0NN
average0NN
community0NN
consumption0.147812295323721NN

The residents who saw this positive feedback reduced their consumption even further Thaler and Sunstein

Label: 12, with a score of: 0.833827420174672
WordScorePoS
positive0JJ
reduce0.022306853314744VB
consumption0.332151194779746NN

Label: 8, with a score of: 0.360687114263513
WordScorePoS
positive0JJ
reduce0.0055149369084567VB
consumption0.147812295323721NN

Label: 11, with a score of: 0.387793160649881
WordScorePoS
positive0.055245183797332JJ
reduce0.0200483235258943VB
consumption0.08955644059472NN

The next component of waste management collection is an essential precursor to further waste treatment

Label: 12, with a score of: 0.699261591226375
WordScorePoS
waste0.138532104381853NN
management0.0339811641917276NN
collection0NN
essential0JJ
treatment0NN

Label: 7, with a score of: 0.382243498025323
WordScorePoS
waste0.0625160381065121NN
management0NN
collection0NN
essential0.0449973847138318JJ
treatment0NN

Label: 11, with a score of: 0.322422732827805
WordScorePoS
waste0.0373517915079663NN
management0.0687164195125178NN
collection0NN
essential0JJ
treatment0NN

Label: 3, with a score of: 0.303676742210627
WordScorePoS
waste0NN
management0.0127591822694525NN
collection0NN
essential0.059903311017139JJ
treatment0.0624189222007589NN

Collection rates in low income cities are low < percent and collection costs represent high fraction percent of municipal waste budgets

Label: 11, with a score of: 0.854375959102191
WordScorePoS
collection0NN
rate0NN
income0NN
city0.607697021770652NN
cost0NN
represent0VB
municipal0.0731385760866977JJ
waste0.0373517915079663NN

Label: 10, with a score of: 0.297134129804624
WordScorePoS
collection0NN
rate0.0172128079963482NN
income0.166475342664294NN
city0NN
cost0.0660824365173543NN
represent0VB
municipal0JJ
waste0NN

Label: 12, with a score of: 0.19776203475634
WordScorePoS
collection0NN
rate0NN
income0NN
city0NN
cost0.0277064208763707NN
represent0VB
municipal0JJ
waste0.138532104381853NN

For middle income cities collection is more widespread percent and represents smaller portion of the MSW budget

Label: 11, with a score of: 0.905209283020355
WordScorePoS
middle0JJ
income0NN
city0.607697021770652NN
collection0NN
represent0VB

Label: 10, with a score of: 0.247977232362653
WordScorePoS
middle0JJ
income0.166475342664294NN
city0NN
collection0NN
represent0VB

High income cities spend smaller portion on collection < percent of MSW budget because they spend much more on processing and treatment and they tend to have high collection rates > percent Hoornweg and Bhada Tata

Label: 11, with a score of: 0.840973285107425
WordScorePoS
income0NN
city0.607697021770652NN
collection0NN
processing0NN
treatment0NN
rate0NN

Label: 10, with a score of: 0.299925131816964
WordScorePoS
income0.166475342664294NN
city0NN
collection0NN
processing0NN
treatment0.0330412182586772NN
rate0.0172128079963482NN

In developing cities where the informal sector plays an important role in providing waste management services integrating these actors to maximize collection and waste treatment is important

Label: 11, with a score of: 0.832736710496421
WordScorePoS
develop0VB
city0.607697021770652NN
sector0NN
provide0.0269743071558856VB
waste0.0373517915079663NN
management0.0687164195125178NN
service0.0269743071558856NN
integrate0.0537696560159885VB
actor0NN
collection0NN
treatment0NN

Label: 12, with a score of: 0.415874739077518
WordScorePoS
develop0VB
city0NN
sector0.0169905820958638NN
provide0.0200087191734851VB
waste0.138532104381853NN
management0.0339811641917276NN
service0.00666957305782837NN
integrate0.0199423462679015VB
actor0.0542519672965169NN
collection0NN
treatment0NN

Cities can choose from number of waste treatment technologies that convert waste to more useful products and mitigate environmental and health risks

Label: 11, with a score of: 0.789720205341617
WordScorePoS
city0.607697021770652NN
waste0.0373517915079663NN
treatment0NN
technology0NN
product0.0268848280079943NN
environmental0.0537696560159885JJ
health0.0229054731708393NN
risk0.0631828116343571NN

Label: 12, with a score of: 0.363465488337315
WordScorePoS
city0NN
waste0.138532104381853NN
treatment0NN
technology0NN
product0.0199423462679015NN
environmental0.0598270388037045JJ
health0.0339811641917276NN
risk0NN

Label: 3, with a score of: 0.301509564344789
WordScorePoS
city0NN
waste0NN
treatment0.0624189222007589NN
technology0NN
product0NN
environmental0JJ
health0.191387734041787NN
risk0.0703902516159815NN

Biogenic waste can become nutrient source through composting electricity via anaerobic digestion or liquid fuel through conversion to ethanol or biodiesel

Label: 12, with a score of: 0.518465941200018
WordScorePoS
waste0.138532104381853NN
nutrient0NN
source0NN
electricity0NN
fuel0.0332151194779746NN

Label: 7, with a score of: 0.686639703617461
WordScorePoS
waste0.0625160381065121NN
nutrient0NN
source0.0274787313211514NN
electricity0.0625160381065121NN
fuel0.0749457204978913NN

Technologies that transform biogenic waste are particularly applicable in less industrialized cities and rural areas

Label: 11, with a score of: 0.837078442280762
WordScorePoS
technology0NN
transform0VB
waste0.0373517915079663NN
applicable0JJ
city0.607697021770652NN
rural0.0373517915079663JJ

Label: 2, with a score of: 0.28941352129451
WordScorePoS
technology0.0130963126055467NN
transform0VB
waste0.0297950283197006NN
applicable0JJ
city0.0440683498519077NN
rural0.148975141598503JJ

Label: 12, with a score of: 0.169932789227383
WordScorePoS
technology0NN
transform0VB
waste0.138532104381853NN
applicable0JJ
city0NN
rural0JJ

Label: 7, with a score of: 0.361675064229174
WordScorePoS
technology0.109914925284606NN
transform0.122412709674631VB
waste0.0625160381065121NN
applicable0JJ
city0NN
rural0JJ

Non biogenic waste can be reused or made into new products via recycling it can also be converted to energy via thermal processes incineration pyrolysis and gasification

Label: 7, with a score of: 0.726600859982779
WordScorePoS
waste0.0625160381065121NN
reuse0NN
product0NN
recycle0VB
energy0.494617163780726NN
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Label: 12, with a score of: 0.541720352275203
WordScorePoS
waste0.138532104381853NN
reuse0.0409791940864438NN
product0.0199423462679015NN
recycle0.0819583881728876VB
energy0.133960988253756NN
process0NN

Recycling is globally ubiquitous though the formality of its practice varies thermal treatment is most appropriate in places that are rich in financial and technical resources

Label: 12, with a score of: 0.553761077524594
WordScorePoS
recycle0.0819583881728876VB
globally0RB
practice0.0703006268489394NN
treatment0NN
technical0JJ
resource0.0416785799329393NN

Label: 6, with a score of: 0.424382315092376
WordScorePoS
recycle0.103230094596636VB
globally0RB
practice0NN
treatment0.0348974218717993NN
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Label: 4, with a score of: 0.356567028950048
WordScorePoS
recycle0VB
globally0RB
practice0NN
treatment0NN
technical0.124876512346911JJ
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Finally all waste management systems need safe method for disposal

Label: 12, with a score of: 0.493228353259687
WordScorePoS
waste0.138532104381853NN
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system0NN
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Label: 11, with a score of: 0.655533005136432
WordScorePoS
waste0.0373517915079663NN
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system0.0315914058171786NN
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Label: 2, with a score of: 0.353571942994185
WordScorePoS
waste0.0297950283197006NN
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safe0.0182713919265625JJ

A continuum of disposal options exist in practice from open dumping which poses the greatest environmental and health hazards to sanitary landfilling engineered facilities designed to collect and treat all of the waste byproducts

Label: 12, with a score of: 0.703424654122624
WordScorePoS
exist0.0332151194779746VB
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Label: 3, with a score of: 0.400846878109953
WordScorePoS
exist0VB
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dump0NN
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Efforts toward sustainable waste management include number of conceptual approaches

Label: 12, with a score of: 0.820763267571069
WordScorePoS
sustainable0.0162522423871414JJ
waste0.138532104381853NN
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Label: 11, with a score of: 0.38466119037204
WordScorePoS
sustainable0.00782503464617421JJ
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The waste hierarchy developed in the ranks environmentally sound waste treatment technologies

Label: 12, with a score of: 0.649026208221318
WordScorePoS
waste0.138532104381853NN
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environmentally0RB
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Label: 7, with a score of: 0.507447263694444
WordScorePoS
waste0.0625160381065121NN
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Two recent schools of thought integrated waste management and industrial ecology aim to see waste as resource

Label: 12, with a score of: 0.580415720340693
WordScorePoS
school0NN
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Label: 9, with a score of: 0.48828445446113
WordScorePoS
school0NN
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Label: 4, with a score of: 0.301720069162705
WordScorePoS
school0.208127520578185NN
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Label: 11, with a score of: 0.351027706060803
WordScorePoS
school0NN
integrate0.0537696560159885VB
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Integrated waste management is set of principles rather than prescription that aims to minimize the environmental and health burden of waste while maximizing its beneficial reuse and safely disposing of waste that is not reused

Label: 12, with a score of: 0.759904626295231
WordScorePoS
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Label: 3, with a score of: 0.231184285788641
WordScorePoS
integrate0VB
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Label: 6, with a score of: 0.324973975940959
WordScorePoS
integrate0.0251182378961834VB
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Label: 11, with a score of: 0.319837081727183
WordScorePoS
integrate0.0537696560159885VB
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The field of industrial ecology proposes that waste management in human systems could mimic natural systems it PART II THE PATH TO SUSTAINABILITY challenges the concept of waste instead viewing all outputs as inputs into other processes

Label: 12, with a score of: 0.507178563036253
WordScorePoS
industrial0JJ
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challenge0NN
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Label: 9, with a score of: 0.4950937392215
WordScorePoS
industrial0.318320101004075JJ
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Label: 11, with a score of: 0.431709439403996
WordScorePoS
industrial0JJ
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Another approach taken by some cities in the context of green growth and sustainable urban development is to use greenhouse gas emissions as metric to assess the sustainability of their waste management systems

Label: 11, with a score of: 0.811323135641958
WordScorePoS
approach0NN
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greenhouse0NN
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Label: 12, with a score of: 0.333743047854067
WordScorePoS
approach0.0542519672965169NN
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green0.0332151194779746JJ
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Label: 13, with a score of: 0.305553052992646
WordScorePoS
approach0NN
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context0.0414107553022249NN
green0.0335649154577099JJ
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development0NN
greenhouse0.0671298309154199NN
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Label: 8, with a score of: 0.159845818787122
WordScorePoS
approach0NN
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green0JJ
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However waste management professionals know that there is no one size fits all solution to waste management

Label: 12, with a score of: 0.710360664513247
WordScorePoS
waste0.138532104381853NN
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Label: 11, with a score of: 0.436758780859255
WordScorePoS
waste0.0373517915079663NN
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Cities need local innovative methods adapted to their residents their waste and their resources

Label: 11, with a score of: 0.932422984362133
WordScorePoS
city0.607697021770652NN
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Label: 12, with a score of: 0.255816325699484
WordScorePoS
city0NN
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Further Reading Annex summarizes policies for reducing emissions and increasing efficiency in cities around the world

Label: 11, with a score of: 0.898045115357743
WordScorePoS
policy0.0136980135278128NN
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Label: 13, with a score of: 0.297566500689733
WordScorePoS
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Annex presents an infrastructure sustainability rating tool created by the Institute for Sustainable Infrastructure ISI and partners

Label: 9, with a score of: 0.839148625380634
WordScorePoS
infrastructure0.192202909044225NN
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Label: 12, with a score of: 0.362936439976339
WordScorePoS
infrastructure0.028867295332595NN
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Annex discusses the concept of engineering for sustainable development

Label: 4, with a score of: 0.742538209986354
WordScorePoS
engineering0.0815069365350084NN
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Building Adaptive and Resilient Cities Key Messages ' Cities regardless of their level of development are particularly vulnerable to disasters and shocks because they concentrate people and enterprises

Label: 11, with a score of: 0.90133690340586
WordScorePoS
building0.0747035830159326NN
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Label: 17, with a score of: 0.217588822969142
WordScorePoS
building0.149940347231585NN
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Many of the world largest cities are located on low lying coasts rivers and other vulnerable areas where the impacts of climate change will be most severe

Label: 13, with a score of: 0.647734157145589
WordScorePoS
world0.00351929966767313NN
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Label: 11, with a score of: 0.626251126025059
WordScorePoS
world0.00626002771693937NN
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Label: 6, with a score of: 0.213745577844903
WordScorePoS
world0.00438651305628668NN
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river0.206460189193271NN
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' The need for cities to reduce their emissions is well established but the need to adapt to climate change and build resilience in cities is not as widely integrated into urban planning

Label: 11, with a score of: 0.851324745549362
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.471800968510544
WordScorePoS
city0NN
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Integrating mitigation and adaptation efforts at the local government level is critical as it will lead to more robust climate change policies and strengthen climate action in cities

Label: 13, with a score of: 0.750819108521946
WordScorePoS
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Label: 11, with a score of: 0.538039270984904
WordScorePoS
integrate0.0537696560159885VB
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' As cities prepare for changing weather patterns they become more resilient

Label: 11, with a score of: 0.762810504702806
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.5759650590921
WordScorePoS
city0NN
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Resilient systems have the capacity to absorb external shocks and continue to function by reorganizing and adapting

Label: 2, with a score of: 0.508569252926269
WordScorePoS
resilient0.0595900566394012JJ
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Label: 1, with a score of: 0.444161350399071
WordScorePoS
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Label: 11, with a score of: 0.441809961072764
WordScorePoS
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Label: 17, with a score of: 0.392392120127481
WordScorePoS
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In fact change and disruptions can create opportunities for development innovation and prosperity in resilient city

Label: 11, with a score of: 0.805189959062365
WordScorePoS
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Label: 13, with a score of: 0.392837843769084
WordScorePoS
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' Urban resilience increasingly depends on multi sectoral partnerships involving local government action private sector participation and community based risk management

Label: 17, with a score of: 0.695719554084321
WordScorePoS
urban0JJ
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Label: 11, with a score of: 0.45170053464996
WordScorePoS
urban0.27622591898666JJ
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While many developing cities will have the opportunity to avoid the path of their partners in the global North and adopt greener growth the threat from climate change is significant

Label: 13, with a score of: 0.640574524007643
WordScorePoS
develop0VB
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Label: 11, with a score of: 0.604101561285088
WordScorePoS
develop0VB
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Label: 8, with a score of: 0.228083077703687
WordScorePoS
develop0VB
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The new wave of urban investment should provide an opportunity for cities to consider and design adequate protection against warming temperatures erratic weather natural disasters sea level rise floods droughts and other potential consequences of climate change

Label: 13, with a score of: 0.714869865360209
WordScorePoS
urban0JJ
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Label: 11, with a score of: 0.582168089429072
WordScorePoS
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Not only have cities have contributed the largest share of the world greenhouse gas emissions they also concentrate many of the people most at risk from the effects of climate change and the enterprises that generate most of the gross world product Satterthwaite et al

Label: 13, with a score of: 0.660443467629346
WordScorePoS
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Label: 11, with a score of: 0.54136716663474
WordScorePoS
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Label: 7, with a score of: 0.308481156510225
WordScorePoS
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These patterns do not necessarily overlap most of the cities that face the highest risks are those with small greenhouse gas contributions and most also have serious constraints on their capacity to adapt to climate change see Box

Label: 13, with a score of: 0.70930836807563
WordScorePoS
pattern0.0414107553022249NN
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Label: 11, with a score of: 0.583530921366837
WordScorePoS
pattern0NN
city0.607697021770652NN
risk0.0631828116343571NN
greenhouse0NN
gas0NN
contribution0NN
constraint0NN
capacity0.0112376090361851NN
climate0.0229054731708393NN
change0.0315914058171786NN

But although the need for cities to reduce their emissions is wellestablished the need to reduce urban residents climate vulnerability is not

Label: 11, with a score of: 0.854638767367986
WordScorePoS
city0.607697021770652NN
reduce0.0200483235258943VB
emission0.0373517915079663NN
urban0.27622591898666JJ
climate0.0229054731708393NN
vulnerability0NN

Label: 13, with a score of: 0.430136689816373
WordScorePoS
city0NN
reduce0.00375696197697663VB
emission0.19598742448524NN
urban0JJ
climate0.291881734282273NN
vulnerability0NN

Thus urban adaptation efforts are only in their initial stages

Label: 11, with a score of: 0.9472546913966
WordScorePoS
urban0.27622591898666JJ
adaptation0.04477822029736NN
stage0NN

At minimum well planned well established successful city will already have taken measures in the past to ensure its ability to withstand extreme weather events

Label: 11, with a score of: 0.725133076828064
WordScorePoS
plan0.0583751976558017NN
establish0VB
successful0JJ
city0.607697021770652NN
measure0NN
ensure0.00501208088147356VB
ability0NN
extreme0JJ
weather0NN
event0NN

Label: 1, with a score of: 0.555386260429742
WordScorePoS
plan0NN
establish0VB
successful0JJ
city0NN
measure0.103895385378356NN
ensure0.0247250190957801VB
ability0NN
extreme0.294526332970048JJ
weather0NN
event0.0908430554521382NN

However urban planners and managers must now consider measures to adapt their cities buildings infrastructure industry institutions and services to the impacts of climate change

Label: 11, with a score of: 0.686324709876067
WordScorePoS
urban0.27622591898666JJ
measure0NN
city0.607697021770652NN
building0.0747035830159326NN
infrastructure0.0194583992186006NN
industry0NN
institution0NN
service0.0269743071558856NN
impact0.0194583992186006NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.514733567192807
WordScorePoS
urban0JJ
measure0.0710409787438328NN
city0NN
building0NN
infrastructure0NN
industry0NN
institution0NN
service0NN
impact0.0437569550806737NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 16, with a score of: 0.262610060372966
WordScorePoS
urban0JJ
measure0NN
city0NN
building0.109790895760528NN
infrastructure0NN
industry0NN
institution0.274477239401321NN
service0NN
impact0.0285977592242194NN
climate0NN
change0NN

Label: 9, with a score of: 0.257555542985741
WordScorePoS
urban0JJ
measure0NN
city0NN
building0NN
infrastructure0.192202909044225NN
industry0.181897200573757NN
institution0NN
service0.0148023490369527NN
impact0.0160169090870187NN
climate0NN
change0NN

Label: 17, with a score of: 0.119547992773205
WordScorePoS
urban0JJ
measure0NN
city0NN
building0.149940347231585NN
infrastructure0.0130185595639838NN
industry0NN
institution0.0249900578719309NN
service0NN
impact0NN
climate0NN
change0NN

There are many ways to do this ranging from adjustments in building codes and land use regulations to the use of insurance to spread risk to effective wellestablished emergency management services Sattherthwaite et al

Label: 17, with a score of: 0.36467785232203
WordScorePoS
range0NN
building0.149940347231585NN
land0NN
regulation0.036961556179878NN
insurance0NN
spread0NN
risk0NN
management0NN
service0NN

Label: 11, with a score of: 0.531682640002994
WordScorePoS
range0NN
building0.0747035830159326NN
land0.0389167984372011NN
regulation0NN
insurance0NN
spread0NN
risk0.0631828116343571NN
management0.0687164195125178NN
service0.0269743071558856NN

Label: 15, with a score of: 0.404957417579267
WordScorePoS
range0NN
building0NN
land0.123873694868802NN
regulation0NN
insurance0NN
spread0NN
risk0.0223459295604043NN
management0.0486060126274891NN
service0.0127200410376242NN

Going forward these 'Adaptation and mitigation linkages Mitigation results in avoiding adverse impacts of climate change in the long run at least the incremental impacts due to greenhouse gas not emitted while adaptation can reduce the unavoidable impacts in the near term but cannot reduce them to zero

Label: 13, with a score of: 0.818249245848694
WordScorePoS
mitigation0.0828215106044499NN
result0NN
avoid0VB
adverse0JJ
impact0.0437569550806737NN
climate0.291881734282273NN
change0.402565546215053NN
due0.0473606524958885JJ
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
adaptation0.0671298309154199NN
reduce0.00375696197697663VB
term0NN

Label: 17, with a score of: 0.102639812256302
WordScorePoS
mitigation0NN
result0NN
avoid0VB
adverse0JJ
impact0NN
climate0NN
change0NN
due0JJ
greenhouse0NN
gas0NN
adaptation0NN
reduce0.00335331148065841VB
term0.149793392961411NN

Label: 7, with a score of: 0.303246935899912
WordScorePoS
mitigation0NN
result0NN
avoid0VB
adverse0JJ
impact0NN
climate0.115011305411453NN
change0.105749654845006NN
due0JJ
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
adaptation0NN
reduce0.00838876601975825VB
term0.0749457204978913NN

Label: 12, with a score of: 0.302156744476018
WordScorePoS
mitigation0NN
result0NN
avoid0VB
adverse0.0664302389559493JJ
impact0.0866018859977851NN
climate0NN
change0NN
due0.0234335422829798JJ
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
adaptation0NN
reduce0.022306853314744VB
term0NN

Failure to mitigate will lead eventually to failure to adapt hence adaptation and mitigation are not alternative strategies but complementary ones that need to be pursued together Sattherthwaite et al

Label: 13, with a score of: 0.65838450829452
WordScorePoS
lead0NN
adaptation0.0671298309154199NN
mitigation0.0828215106044499NN
alternative0NN
strategy0.0201523636533764NN
pursue0VB

'Adaptive capacity Inherent capacity of system such as city government population such as low income community in city or individual household to take actions that can help avoid loss and speed recovery from any impact of climate change

Label: 11, with a score of: 0.75080031747926
WordScorePoS
capacity0.0112376090361851NN
system0.0315914058171786NN
city0.607697021770652NN
government0NN
population0.0194583992186006NN
income0NN
community0NN
individual0JJ
household0NN
action0NN
avoid0VB
loss0.0315914058171786NN
recovery0NN
impact0.0194583992186006NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.475788203520615
WordScorePoS
capacity0.025270503924109NN
system0NN
city0NN
government0NN
population0NN
income0NN
community0.0343390275626203NN
individual0JJ
household0NN
action0.0369195714265843NN
avoid0VB
loss0.0236803262479443NN
recovery0NN
impact0.0437569550806737NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 10, with a score of: 0.20732390307363
WordScorePoS
capacity0NN
system0NN
city0NN
government0NN
population0.0860640399817412NN
income0.166475342664294NN
community0.0202620733250388NN
individual0.064698038783335JJ
household0.0330412182586772NN
action0.0145231653596925NN
avoid0VB
loss0NN
recovery0NN
impact0NN
climate0NN
change0NN

Elements of adaptive capacity include knowledge institutional capacity and financial and technological resources

Label: 13, with a score of: 0.548669095889776
WordScorePoS
adaptive0.0548233071065585JJ
capacity0.025270503924109NN
include0VB
knowledge0NN
institutional0.0828215106044499JJ
technological0.0473606524958885JJ
resource0NN

Label: 17, with a score of: 0.500240291649869
WordScorePoS
adaptive0JJ
capacity0.0451108483130998NN
include0VB
knowledge0.0499801157438617NN
institutional0.036961556179878JJ
technological0JJ
resource0.0375923735942499NN

Label: 2, with a score of: 0.305849542919038
WordScorePoS
adaptive0JJ
capacity0.0179281831452421NN
include0VB
knowledge0.0595900566394012NN
institutional0JJ
technological0JJ
resource0.0358563662904843NN

Low income populations in city will tend to have lower adaptive capacity because they are unable to afford goodquality housing on safe sites and avoid dangerous livelihoods

Label: 11, with a score of: 0.905093837619
WordScorePoS
income0NN
population0.0194583992186006NN
city0.607697021770652NN
adaptive0JJ
capacity0.0112376090361851NN
housing0.219415728260093NN
safe0.0916218926833571JJ
avoid0VB
livelihood0NN

Label: 10, with a score of: 0.260062087147638
WordScorePoS
income0.166475342664294NN
population0.0860640399817412NN
city0NN
adaptive0JJ
capacity0NN
housing0NN
safe0.0202620733250388JJ
avoid0VB
livelihood0NN

There is also wide range in adaptive capacity among city and national governments relating to the resources available to them the information base to guide action the infrastructure in place and the quality of their institutions and governance systems Sattherthwaite et al

Label: 11, with a score of: 0.665347893597124
WordScorePoS
wide0JJ
range0NN
adaptive0JJ
capacity0.0112376090361851NN
city0.607697021770652NN
national0.00501208088147356JJ
government0NN
resource0.0449504361447405NN
information0NN
base0NN
action0NN
infrastructure0.0194583992186006NN
quality0.0136980135278128NN
institution0NN
governance0NN
system0.0315914058171786NN

Label: 16, with a score of: 0.409992535688487
WordScorePoS
wide0JJ
range0NN
adaptive0JJ
capacity0.016515769558553NN
city0NN
national0.0294647633960925JJ
government0NN
resource0NN
information0.0241291244414115NN
base0NN
action0NN
infrastructure0NN
quality0NN
institution0.274477239401321NN
governance0.107490825192354NN
system0NN

Label: 9, with a score of: 0.322244737467228
WordScorePoS
wide0JJ
range0.0368585655748969NN
adaptive0JJ
capacity0NN
city0NN
national0.00412562425683033JJ
government0NN
resource0.0185001613201543NN
information0.0675706773907211NN
base0NN
action0.0135141354781442NN
infrastructure0.192202909044225NN
quality0.022550656390892NN
institution0NN
governance0NN
system0NN

BOX Glossary of Terms Related to Adaptation ecosystem services

Label: 17, with a score of: 0.475663000551863
WordScorePoS
term0.149793392961411NN
related0JJ
adaptation0NN
ecosystem0NN
service0NN

Label: 15, with a score of: 0.466143685883543
WordScorePoS
term0NN
related0JJ
adaptation0NN
ecosystem0.134075577362426NN
service0.0127200410376242NN

Label: 7, with a score of: 0.381350154832437
WordScorePoS
term0.0749457204978913NN
related0JJ
adaptation0NN
ecosystem0NN
service0.0451471467893167NN

Many investments being made in cities are in fact maladaptive rather than adaptive as they decrease resilience to climate change

Label: 13, with a score of: 0.673050077353428
WordScorePoS
investment0NN
city0NN
adaptive0.0548233071065585JJ
decrease0NN
resilience0.0279982034978914NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 11, with a score of: 0.669080262244689
WordScorePoS
investment0NN
city0.607697021770652NN
adaptive0JJ
decrease0.0731385760866977NN
resilience0.0373517915079663NN
climate0.0229054731708393NN
change0.0315914058171786NN

Removing maladaptation is often the first task to be addressed even prior to new adaptations Sattherthwaite et al

Label: 0, with a score of: 1

'Resilience Resilience is product of governments enterprises populations and individuals with strong adaptive capacity

Label: 10, with a score of: 0.513517641094998
WordScorePoS
resilience0NN
product0NN
government0NN
enterprise0NN
population0.0860640399817412NN
individual0.064698038783335JJ
strong0.0488696285210061JJ
adaptive0JJ
capacity0NN

Label: 9, with a score of: 0.46477448201942
WordScorePoS
resilience0NN
product0.0442597400933733NN
government0NN
enterprise0.0909486002868787NN
population0NN
individual0JJ
strong0.0454743001434393JJ
adaptive0JJ
capacity0NN

Label: 8, with a score of: 0.450513165409945
WordScorePoS
resilience0NN
product0.0591643007226199NN
government0NN
enterprise0.0607878664258118NN
population0.0428212738652355NN
individual0JJ
strong0JJ
adaptive0JJ
capacity0.0123650647908625NN

It indicates capacity to maintain core functions in the face of hazardous threats and impacts especially for vulnerable populations

Label: 2, with a score of: 0.492200326695356
WordScorePoS
capacity0.0179281831452421NN
maintain0.0881366997038154VB
function0.0440683498519077NN
hazardous0JJ
threat0NN
impact0NN
vulnerable0.0182713919265625JJ
population0.0310434135749871NN

Label: 10, with a score of: 0.42205551024813
WordScorePoS
capacity0NN
maintain0VB
function0NN
hazardous0JJ
threat0.064698038783335NN
impact0NN
vulnerable0.0202620733250388JJ
population0.0860640399817412NN

Label: 11, with a score of: 0.37315922825489
WordScorePoS
capacity0.0112376090361851NN
maintain0.055245183797332VB
function0NN
hazardous0JJ
threat0NN
impact0.0194583992186006NN
vulnerable0.0458109463416786JJ
population0.0194583992186006NN

Label: 6, with a score of: 0.340691343019089
WordScorePoS
capacity0.0104991907358033NN
maintain0VB
function0NN
hazardous0.0516150472983178JJ
threat0NN
impact0.0181797964452808NN
vulnerable0.0214003646985842JJ
population0.0363595928905615NN

It usually requires capacity to anticipate climate change and to plan needed adaptations

Label: 13, with a score of: 0.914016241523061
WordScorePoS
require0.0473606524958885VB
capacity0.025270503924109NN
climate0.291881734282273NN
change0.402565546215053NN
plan0.0291713033871158NN
adaptation0.0671298309154199NN

The resilience of any population group to climate change interacts with its resilience to other dynamic pressures including economic change conflict and violence

Label: 13, with a score of: 0.857460677763486
WordScorePoS
resilience0.0279982034978914NN
population0NN
climate0.291881734282273NN
change0.402565546215053NN
pressure0NN
include0VB
economic0JJ
violence0NN

Label: 8, with a score of: 0.127379928755971
WordScorePoS
resilience0NN
population0.0428212738652355NN
climate0NN
change0NN
pressure0NN
include0VB
economic0.128463821595706JJ
violence0NN

Label: 16, with a score of: 0.122472621821489
WordScorePoS
resilience0NN
population0NN
climate0NN
change0NN
pressure0NN
include0VB
economic0JJ
violence0.164686343640793NN

'Urban resilience The degree to which cities are able to tolerate alteration before reorganizing around new set of structures and processes Alberti et al

Label: 11, with a score of: 0.920852415430866
WordScorePoS
resilience0.0373517915079663NN
degree0NN
city0.607697021770652NN
set0NN
structure0NN
process0NN

Label: 13, with a score of: 0.274761745131918
WordScorePoS
resilience0.0279982034978914NN
degree0.164469921319675NN
city0NN
set0NN
structure0NN
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As such it can be measured by how well city balances ecosystem economic and social functions by how it responds to gradual impacts like climate change or sudden impacts like natural disasters and by its ability to capitalize on positive opportunities that emerge as result of change Berkes and Folke Barnett Alberti et al

Label: 13, with a score of: 0.660443354304279
WordScorePoS
measure0.0710409787438328NN
city0NN
ecosystem0NN
economic0JJ
social0JJ
function0NN
impact0.0437569550806737NN
climate0.291881734282273NN
change0.402565546215053NN
natural0.0171695137813101JJ
disaster0.0236803262479443NN
ability0NN
positive0JJ
opportunity0NN
emerge0VB
result0NN

Label: 11, with a score of: 0.545075103686961
WordScorePoS
measure0NN
city0.607697021770652NN
ecosystem0NN
economic0.0389167984372011JJ
social0.0458109463416786JJ
function0NN
impact0.0194583992186006NN
climate0.0229054731708393NN
change0.0315914058171786NN
natural0.0229054731708393JJ
disaster0.157957029085893NN
ability0NN
positive0.055245183797332JJ
opportunity0.0164178645080222NN
emerge0VB
result0NN

Label: 15, with a score of: 0.169923686827277
WordScorePoS
measure0.0223459295604043NN
city0NN
ecosystem0.134075577362426NN
economic0JJ
social0JJ
function0NN
impact0.0137637438743113NN
climate0.032404008418326NN
change0.0446918591208085NN
natural0.016202004209163JJ
disaster0NN
ability0NN
positive0JJ
opportunity0.0116130458375813NN
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result0NN

Label: 8, with a score of: 0.158205638141634
WordScorePoS
measure0.0347609334428716NN
city0NN
ecosystem0NN
economic0.128463821595706JJ
social0.0504071033101074JJ
function0NN
impact0NN
climate0NN
change0NN
natural0JJ
disaster0NN
ability0.0607878664258118NN
positive0JJ
opportunity0.0361300980868102NN
emerge0VB
result0NN

'Maladaptation Actions or investments that 'Vulnerability The propensity of social and ecolog increase rather than reduce vulnerability to impacts of climate change

Label: 13, with a score of: 0.798025636370011
WordScorePoS
action0.0369195714265843NN
investment0NN
social0JJ
increase0.0145316075945746NN
reduce0.00375696197697663VB
vulnerability0NN
impact0.0437569550806737NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 1, with a score of: 0.354118008236315
WordScorePoS
action0.0269969049497485NN
investment0.0376648791150643NN
social0.112994637345193JJ
increase0NN
reduce0.0164833460638534VB
vulnerability0.120266261535276NN
impact0NN
climate0.0376648791150643NN
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This can include the shifting of vulnerability from one social group or place to another it also includes shifting risk to future generations and or to ecosystems and ical systems to suffer harm from exposure to external stresses and shocks Stockholm Resilience Institute

Label: 15, with a score of: 0.194950619076182
WordScorePoS
include0VB
vulnerability0NN
social0JJ
risk0.0223459295604043NN
future0NN
generation0NN
ecosystem0.134075577362426NN
system0NN
suffer0VB
harm0NN
exposure0NN
external0JJ
stress0NN
shock0NN
resilience0NN

Label: 1, with a score of: 0.796531312933613
WordScorePoS
include0VB
vulnerability0.120266261535276NN
social0.112994637345193JJ
risk0.0519476926891779NN
future0NN
generation0NN
ecosystem0NN
system0.0519476926891779NN
suffer0VB
harm0NN
exposure0.120266261535276NN
external0JJ
stress0NN
shock0.120266261535276NN
resilience0.0614198493690006NN

The term is often used as an antonym of resilience

Label: 17, with a score of: 0.786065339944212
WordScorePoS
term0.149793392961411NN
resilience0NN

Label: 7, with a score of: 0.393289931523987
WordScorePoS
term0.0749457204978913NN
resilience0NN

Label: 1, with a score of: 0.322310709565165
WordScorePoS
term0NN
resilience0.0614198493690006NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD adaptation measures need to be integrated with plans to mitigate climate change through more efficient city systems Box

Label: 11, with a score of: 0.667117057856118
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
adaptation0.04477822029736NN
measure0NN
integrate0.0537696560159885VB
plan0.0583751976558017NN
climate0.0229054731708393NN
change0.0315914058171786NN
city0.607697021770652NN
system0.0315914058171786NN

Label: 13, with a score of: 0.634028049175945
WordScorePoS
building0NN
sustainability0NN
world0.00351929966767313NN
adaptation0.0671298309154199NN
measure0.0710409787438328NN
integrate0.0201523636533764VB
plan0.0291713033871158NN
climate0.291881734282273NN
change0.402565546215053NN
city0NN
system0NN

Label: 17, with a score of: 0.161284929139127
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
adaptation0NN
measure0NN
integrate0VB
plan0.0130185595639838NN
climate0NN
change0NN
city0NN
system0.0211360962287062NN

Climate Change Vulnerability in Urban Areas BOX There is consensus that large scale disasters are increasing in frequency worldwide largely due to weather related events

Label: 13, with a score of: 0.763698290715197
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
vulnerability0NN
urban0JJ
consensus0NN
scale0NN
disaster0.0236803262479443NN
increase0.0145316075945746NN
worldwide0RB
due0.0473606524958885JJ
weather0.0671298309154199NN
related0.0168470026160727JJ
event0.0414107553022249NN

Label: 11, with a score of: 0.424398580355382
WordScorePoS
climate0.0229054731708393NN
change0.0315914058171786NN
vulnerability0NN
urban0.27622591898666JJ
consensus0NN
scale0NN
disaster0.157957029085893NN
increase0.00323105002784081NN
worldwide0RB
due0JJ
weather0NN
related0.0112376090361851JJ
event0NN

Label: 1, with a score of: 0.313058281498302
WordScorePoS
climate0.0376648791150643NN
change0NN
vulnerability0.120266261535276NN
urban0JJ
consensus0NN
scale0NN
disaster0.0519476926891779NN
increase0NN
worldwide0RB
due0.0519476926891779JJ
weather0NN
related0.0184786920895884JJ
event0.0908430554521382NN

Many of the extreme weather events that have caused significant economic and human loss in the past years have taken place in urban areas or affected them indirectly for example through immigration from affected areas or interrupted service provision

Label: 1, with a score of: 0.646930958490488
WordScorePoS
extreme0.294526332970048JJ
weather0NN
event0.0908430554521382NN
cause0NN
economic0.0959899298575892JJ
human0JJ
loss0NN
urban0JJ
indirectly0RB
service0.0443555132287109NN
provision0NN

Label: 11, with a score of: 0.548691592480587
WordScorePoS
extreme0JJ
weather0NN
event0NN
cause0.04477822029736NN
economic0.0389167984372011JJ
human0.0273960270556257JJ
loss0.0315914058171786NN
urban0.27622591898666JJ
indirectly0RB
service0.0269743071558856NN
provision0NN

Label: 8, with a score of: 0.200981283845675
WordScorePoS
extreme0JJ
weather0NN
event0NN
cause0NN
economic0.128463821595706JJ
human0.0150723186962745JJ
loss0NN
urban0JJ
indirectly0RB
service0.0197870712294258NN
provision0NN

Some of these weather events are considered to be early manifestations of climate change and in future profound modifications of the climate will in the future have considerably greater impacts on cities from flooding to heat waves

Label: 13, with a score of: 0.750401282917185
WordScorePoS
weather0.0671298309154199NN
event0.0414107553022249NN
manifestation0NN
climate0.291881734282273NN
change0.402565546215053NN
future0NN
profound0JJ
impact0.0437569550806737NN
city0NN

Label: 11, with a score of: 0.513564977271853
WordScorePoS
weather0NN
event0NN
manifestation0NN
climate0.0229054731708393NN
change0.0315914058171786NN
future0.0373517915079663NN
profound0JJ
impact0.0194583992186006NN
city0.607697021770652NN

According to the Intergovernmental Panel on Climate Change Parry et al

Label: 13, with a score of: 0.949953422699976
WordScorePoS
intergovernmental0.0828215106044499JJ
panel0.0548233071065585NN
climate0.291881734282273NN
change0.402565546215053NN

the estimated economic impacts of extreme weather in the past range from percent GDP loss in Central America as result of an El Niño year to percent GDP loss for Honduras during Hurricane Mitch

Label: 1, with a score of: 0.572585901409493
WordScorePoS
estimate0NN
economic0.0959899298575892JJ
impact0NN
extreme0.294526332970048JJ
weather0NN
range0NN
gdp0NN
loss0NN
central0JJ
result0NN

Label: 8, with a score of: 0.188357259578077
WordScorePoS
estimate0NN
economic0.128463821595706JJ
impact0NN
extreme0JJ
weather0NN
range0NN
gdp0NN
loss0NN
central0JJ
result0NN

Label: 13, with a score of: 0.330454475894146
WordScorePoS
estimate0NN
economic0JJ
impact0.0437569550806737NN
extreme0.0335649154577099JJ
weather0.0671298309154199NN
range0.0335649154577099NN
gdp0NN
loss0.0236803262479443NN
central0JJ
result0NN

These aggregate figures obscure impacts much higher than the average in some locations Sattherthwaite et al

Label: 13, with a score of: 0.744952072156123
WordScorePoS
impact0.0437569550806737NN
average0.1656430212089NN
location0NN

Label: 10, with a score of: 0.521569271553487
WordScorePoS
impact0NN
average0.146608885563018NN
location0NN

Label: 12, with a score of: 0.308091030237097
WordScorePoS
impact0.0866018859977851NN
average0NN
location0NN

Disasters are product of hazards interacting with vulnerable population understanding the intricacies of local vulnerabilities along with the hazards to which city is exposed Figure is essential to build resilience in urban centers

Label: 11, with a score of: 0.896547707358624
WordScorePoS
disaster0.157957029085893NN
product0.0268848280079943NN
hazard0NN
vulnerable0.0458109463416786JJ
population0.0194583992186006NN
local0.0194583992186006JJ
vulnerability0NN
city0.607697021770652NN
figure0NN
essential0JJ
build0VB
resilience0.0373517915079663NN
urban0.27622591898666JJ

Label: 1, with a score of: 0.307206197048186
WordScorePoS
disaster0.0519476926891779NN
product0NN
hazard0NN
vulnerable0.112994637345193JJ
population0NN
local0JJ
vulnerability0.120266261535276NN
city0NN
figure0NN
essential0JJ
build0.0614198493690006VB
resilience0.0614198493690006NN
urban0JJ

Benefits of Integrating Mitigation and Adaptation Mitigation will not progress quickly enough to avoid significant climate change impacts hence adaptation is necessary

Label: 13, with a score of: 0.906241379142094
WordScorePoS
integrate0.0201523636533764VB
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN
progress0NN
quickly0RB
avoid0VB
climate0.291881734282273NN
change0.402565546215053NN
impact0.0437569550806737NN

At the same time given the scale of expected impacts adaptation cannot be the only response to climate change

Label: 13, with a score of: 0.906736178257256
WordScorePoS
time0NN
scale0NN
expect0.0335649154577099VB
impact0.0437569550806737NN
adaptation0.0671298309154199NN
response0.0548233071065585NN
climate0.291881734282273NN
change0.402565546215053NN

Both approaches are not only necessary but complementary Wilbanks and Sathaye

Label: 12, with a score of: 1
WordScorePoS
approach0.0542519672965169NN

Linking mitigation and adaptation at the local government level is expected to enhance and strengthen the potential impacts of both types of climate action

Label: 13, with a score of: 0.776780004854309
WordScorePoS
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN
local0.0145856516935579JJ
government0NN
level0.0415277415054218NN
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enhance0VB
strengthen0.00673981184904291VB
potential0NN
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type0NN
climate0.291881734282273NN
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By not coordinating mitigation and adaptation efforts urban planners run the risk of increasing local greenhouse gas emissions through adaptation measures or increasing local vulnerability as result of mitigation measures

Label: 13, with a score of: 0.704899539387307
WordScorePoS
coordinate0.0414107553022249NN
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN
effort0.0171695137813101NN
urban0JJ
risk0NN
increase0.0145316075945746NN
local0.0145856516935579JJ
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
measure0.0710409787438328NN
vulnerability0NN
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Label: 11, with a score of: 0.511474749826567
WordScorePoS
coordinate0NN
mitigation0.055245183797332NN
adaptation0.04477822029736NN
effort0.0229054731708393NN
urban0.27622591898666JJ
risk0.0631828116343571NN
increase0.00323105002784081NN
local0.0194583992186006JJ
greenhouse0NN
gas0NN
emission0.0373517915079663NN
measure0NN
vulnerability0NN
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Label: 1, with a score of: 0.301294871791811
WordScorePoS
coordinate0NN
mitigation0NN
adaptation0NN
effort0NN
urban0JJ
risk0.0519476926891779NN
increase0NN
local0JJ
greenhouse0NN
gas0NN
emission0NN
measure0.103895385378356NN
vulnerability0.120266261535276NN
result0NN

As an example stand alone mitigation strategy might recommend higher concentration of housing developments close to the city center while an adaptation strategy could shed light on the fact that the city center is located in flood prone area where high density housing increases the vulnerability of local residents to disasters Bizikova et al

Label: 11, with a score of: 0.967736586435269
WordScorePoS
stand0VB
mitigation0.055245183797332NN
strategy0NN
recommend0VB
concentration0NN
housing0.219415728260093NN
development0.0100241617629471NN
city0.607697021770652NN
adaptation0.04477822029736NN
density0.0731385760866977NN
increase0.00323105002784081NN
vulnerability0NN
local0.0194583992186006JJ
disaster0.157957029085893NN

Integrating mitigation and adaptation efforts minimizes the chances of duplication or maladaptation and produces more robust climate change policies to reduce the costs and negative impacts of climate change

Label: 13, with a score of: 0.90299796424549
WordScorePoS
integrate0.0201523636533764VB
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN
effort0.0171695137813101NN
minimize0VB
chance0.0548233071065585NN
produce0VB
climate0.291881734282273NN
change0.402565546215053NN
policy0.0102677744436108NN
reduce0.00375696197697663VB
cost0.0279982034978914NN
impact0.0437569550806737NN

For instance improvements in building energy efficiency can reinforce both adaptation and mitigation goals Wilbanks and Sathaye

Label: 7, with a score of: 0.80665678876706
WordScorePoS
improvement0.0924643738905717NN
building0NN
energy0.494617163780726NN
reinforce0VB
adaptation0NN
mitigation0NN
goal0NN

Label: 17, with a score of: 0.271897924424107
WordScorePoS
improvement0NN
building0.149940347231585NN
energy0.0109843026967392NN
reinforce0.036961556179878VB
adaptation0NN
mitigation0NN
goal0NN

Label: 12, with a score of: 0.184063939454589
WordScorePoS
improvement0NN
building0NN
energy0.133960988253756NN
reinforce0VB
adaptation0NN
mitigation0NN
goal0NN

Label: 11, with a score of: 0.307751895311892
WordScorePoS
improvement0NN
building0.0747035830159326NN
energy0.0492535935240667NN
reinforce0VB
adaptation0.04477822029736NN
mitigation0.055245183797332NN
goal0NN

When the relationships between mitigation and adaptation are ignored there is risk of failure in both areas

Label: 11, with a score of: 0.660881238747444
WordScorePoS
mitigation0.055245183797332NN
adaptation0.04477822029736NN
risk0.0631828116343571NN

Label: 13, with a score of: 0.607207439329492
WordScorePoS
mitigation0.0828215106044499NN
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risk0NN

PART II THE PATH TO SUSTAINABILITY Effect of Respondents Top Three Sectors Affected Temperature Increase Heatwaves Human Health Energy Water More Frequent Intense Rainfall Buildings Water Transport Sea Level Rise Buildings Waste Transport Storms and Floods Human Health Buildings Water Drought Water Human Health Energy Source Adapted from CDP

Label: 6, with a score of: 0.72203352014003
WordScorePoS
sustainability0NN
effect0NN
sector0.0214003646985842NN
affect0.0209983814716066VB
temperature0NN
increase0.00905621749519576NN
human0.0127979231406811JJ
health0NN
energy0.0153390539205676NN
water0.556409482163189NN
building0.0348974218717993NN
transport0NN
sea0.041835863322702NN
level0.00647010760354177NN
rise0.0251182378961834NN
waste0NN
flood0.041835863322702NN
drought0.041835863322702NN
source0.0613562156822703NN

Label: 12, with a score of: 0.3871434468966
WordScorePoS
sustainability0.0409791940864438NN
effect0NN
sector0.0169905820958638NN
affect0.0166714319731757VB
temperature0NN
increase0.00958677785775002NN
human0.0101607690729067JJ
health0.0339811641917276NN
energy0.133960988253756NN
water0.186896403054502NN
building0NN
transport0.0277064208763707NN
sea0NN
level0.00513687014008325NN
rise0NN
waste0.138532104381853NN
flood0NN
drought0NN
source0NN

Label: 7, with a score of: 0.296313933746241
WordScorePoS
sustainability0NN
effect0NN
sector0NN
affect0VB
temperature0NN
increase0.0162235147372888NN
human0JJ
health0NN
energy0.494617163780726NN
water0NN
building0NN
transport0NN
sea0NN
level0NN
rise0NN
waste0.0625160381065121NN
flood0NN
drought0NN
source0.0274787313211514NN

Label: 3, with a score of: 0.202311415334189
WordScorePoS
sustainability0NN
effect0NN
sector0NN
affect0.00625975725959118VB
temperature0NN
increase0.0143984997539974NN
human0JJ
health0.191387734041787NN
energy0NN
water0.0382775468083574NN
building0NN
transport0NN
sea0NN
level0NN
rise0NN
waste0NN
flood0NN
drought0NN
source0NN

Label: 13, with a score of: 0.185037759880542
WordScorePoS
sustainability0NN
effect0NN
sector0NN
affect0.0336940052321454VB
temperature0.207053776511125NN
increase0.0145316075945746NN
human0.0205355488872215JJ
health0NN
energy0.0123065238088614NN
water0NN
building0NN
transport0NN
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
waste0NN
flood0NN
drought0NN
source0.0123065238088614NN

Label: 17, with a score of: 0.171470058437334
WordScorePoS
sustainability0.036961556179878NN
effect0NN
sector0.0459744293658165NN
affect0VB
temperature0NN
increase0.00432344066632653NN
human0JJ
health0NN
energy0.0109843026967392NN
water0NN
building0.149940347231585NN
transport0.0249900578719309NN
sea0NN
level0.0138997399903163NN
rise0NN
waste0NN
flood0NN
drought0NN
source0.0109843026967392NN

Cities face two types of climate related risks catastrophic and systemic

Label: 11, with a score of: 0.866025256980925
WordScorePoS
city0.607697021770652NN
type0NN
climate0.0229054731708393NN
related0.0112376090361851JJ
risk0.0631828116343571NN
catastrophic0JJ
systemic0JJ

Label: 13, with a score of: 0.379231479983432
WordScorePoS
city0NN
type0NN
climate0.291881734282273NN
related0.0168470026160727JJ
risk0NN
catastrophic0JJ
systemic0JJ

Catastrophic risks originate from poor design and location of the built environment including infrastructure

Label: 1, with a score of: 0.552459713713275
WordScorePoS
catastrophic0JJ
risk0.0519476926891779NN
poor0.150659516460257JJ
location0NN
build0.0614198493690006VB
environment0NN
include0VB
infrastructure0NN

Label: 9, with a score of: 0.512810824994107
WordScorePoS
catastrophic0JJ
risk0NN
poor0JJ
location0NN
build0.030745604615229VB
environment0.0221298700466866NN
include0VB
infrastructure0.192202909044225NN

These risks include losses associated with violent winds temperature extremes and sea level rise

Label: 13, with a score of: 0.787232903384174
WordScorePoS
risk0NN
include0VB
loss0.0236803262479443NN
temperature0.207053776511125NN
extreme0.0335649154577099JJ
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN

Label: 1, with a score of: 0.473825555416289
WordScorePoS
risk0.0519476926891779NN
include0VB
loss0NN
temperature0NN
extreme0.294526332970048JJ
sea0NN
level0.0113874611101829NN
rise0NN

An example is the Hurricane Katrina disaster that in New Orleans alone caused nearly deaths loss of more than jobs and billion in lost wages with total financial losses estimated at billion

Label: 11, with a score of: 0.510798446597572
WordScorePoS
disaster0.157957029085893NN
cause0.04477822029736NN
death0.0315914058171786NN
loss0.0315914058171786NN
job0.0136980135278128NN
lose0VB
total0NN
estimate0NN

Label: 3, with a score of: 0.416042068817405
WordScorePoS
disaster0NN
cause0NN
death0.193573191943949NN
loss0NN
job0NN
lose0VB
total0NN
estimate0.059903311017139NN

Label: 8, with a score of: 0.222649702087469
WordScorePoS
disaster0NN
cause0NN
death0NN
loss0NN
job0.135650868266471NN
lose0VB
total0NN
estimate0NN

Label: 15, with a score of: 0.431542662801268
WordScorePoS
disaster0NN
cause0.0316735178673487NN
death0NN
loss0.0670377886812128NN
job0NN
lose0.0781544824456958VB
total0NN
estimate0.0190167691930804NN

The floods and landslides in Rio de Janeiro and São Paulo left more than dead and homeless

Label: 0, with a score of: 1

In Australia in Cyclone Yasi produced damages over billion due to flooding alone with large percentage of this occurring in urban areas ICLEI

Label: 11, with a score of: 0.82840812702594
WordScorePoS
produce0VB
due0JJ
percentage0NN
occur0VB
urban0.27622591898666JJ

Of the megacities in are in low elevation coastal zones that are most vulnerable to the sea level rise and storm surges associated with climate change Figure

Label: 13, with a score of: 0.883727208712747
WordScorePoS
coastal0JJ
vulnerable0.0171695137813101JJ
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
climate0.291881734282273NN
change0.402565546215053NN
figure0NN

Label: 14, with a score of: 0.331168881828814
WordScorePoS
coastal0.265991560286306JJ
vulnerable0JJ
sea0.0977100948994686NN
level0.010074208709011NN
rise0NN
climate0.0166605993001162NN
change0NN
figure0NN

Many other large cities are close to river estuaries coastal areas and other vulnerable sites due to the historical advantages of these locations

Label: 11, with a score of: 0.823058191976336
WordScorePoS
city0.607697021770652NN
river0NN
coastal0JJ
vulnerable0.0458109463416786JJ
due0JJ
historical0JJ
location0NN

Label: 6, with a score of: 0.361324628173757
WordScorePoS
city0NN
river0.206460189193271NN
coastal0JJ
vulnerable0.0214003646985842JJ
due0.0590310971343993JJ
historical0JJ
location0NN

Label: 14, with a score of: 0.33500208623712
WordScorePoS
city0NN
river0NN
coastal0.265991560286306JJ
vulnerable0JJ
due0JJ
historical0JJ
location0NN

And continued urbanization is putting an ever greater concentration of population and assets in these high risk sites Box

Label: 11, with a score of: 0.720554167242722
WordScorePoS
continue0.0537696560159885VB
urbanization0.146277152173395NN
put0VBN
concentration0NN
population0.0194583992186006NN
asset0NN
risk0.0631828116343571NN

Label: 10, with a score of: 0.404556850137136
WordScorePoS
continue0.0237821918091848VB
urbanization0NN
put0VBN
concentration0NN
population0.0860640399817412NN
asset0.0488696285210061NN
risk0NN

Within cities competition for land drives poorer urban dwellers to settle in higher density marginal areas such as steep hillsides or flood plains which further increases exposure

Label: 11, with a score of: 0.969020294503645
WordScorePoS
city0.607697021770652NN
land0.0389167984372011NN
drive0NN
urban0.27622591898666JJ
density0.0731385760866977NN
increase0.00323105002784081NN
exposure0NN

This combination of higher degree of exposure and vulnerability of both people and assets within cities results in evergreater social and economic impacts of natural hazards

Label: 11, with a score of: 0.704434116116812
WordScorePoS
degree0NN
exposure0NN
vulnerability0NN
people0.0161552501392041NNS
asset0NN
city0.607697021770652NN
result0NN
social0.0458109463416786JJ
economic0.0389167984372011JJ
impact0.0194583992186006NN
natural0.0229054731708393JJ
hazard0NN

Label: 13, with a score of: 0.274223926135901
WordScorePoS
degree0.164469921319675NN
exposure0NN
vulnerability0NN
people0.0121096729954788NNS
asset0NN
city0NN
result0NN
social0JJ
economic0JJ
impact0.0437569550806737NN
natural0.0171695137813101JJ
hazard0.0548233071065585NN

Label: 8, with a score of: 0.177797613192547
WordScorePoS
degree0NN
exposure0NN
vulnerability0NN
people0.0106656521351091NNS
asset0NN
city0NN
result0NN
social0.0504071033101074JJ
economic0.128463821595706JJ
impact0NN
natural0JJ
hazard0NN

Label: 1, with a score of: 0.491895979937174
WordScorePoS
degree0NN
exposure0.120266261535276NN
vulnerability0.120266261535276NN
people0.0371911007748996NNS
asset0NN
city0NN
result0NN
social0.112994637345193JJ
economic0.0959899298575892JJ
impact0NN
natural0.0376648791150643JJ
hazard0NN

Thus emerging urbanizing cities should prioritize risk mapping and develop policies in accordance with these findings

Label: 11, with a score of: 0.935230118185873
WordScorePoS
emerge0VB
city0.607697021770652NN
risk0.0631828116343571NN
develop0VB
policy0.0136980135278128NN
accordance0NN

For instance if hazard map indicates flood risk in an up andcoming coastal area of the city urban planners can take preemptive measures and enact policies to ensure that industrial and residential areas are developed elsewhere

Label: 11, with a score of: 0.857412628875712
WordScorePoS
hazard0NN
risk0.0631828116343571NN
coastal0JJ
city0.607697021770652NN
urban0.27622591898666JJ
measure0NN
policy0.0136980135278128NN
ensure0.00501208088147356VB
industrial0JJ
residential0JJ
develop0VB

Label: 9, with a score of: 0.296264186079515
WordScorePoS
hazard0NN
risk0NN
coastal0JJ
city0NN
urban0JJ
measure0NN
policy0.011275328195446NN
ensure0.00412562425683033VB
industrial0.318320101004075JJ
residential0JJ
develop0VB

Label: 14, with a score of: 0.236136654538488
WordScorePoS
hazard0NN
risk0NN
coastal0.265991560286306JJ
city0NN
urban0JJ
measure0NN
policy0NN
ensure0VB
industrial0JJ
residential0JJ
develop0VB

FIG

Label: 0, with a score of: 1

Physical Effects of Climate Change Identified by Cities BUILDING SUSTAINABILITY IN AN URBANIZING WORLD FIG

Label: 11, with a score of: 0.679158895294669
WordScorePoS
effect0NN
climate0.0229054731708393NN
change0.0315914058171786NN
city0.607697021770652NN
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN

Label: 13, with a score of: 0.637859678745713
WordScorePoS
effect0NN
climate0.291881734282273NN
change0.402565546215053NN
city0NN
building0NN
sustainability0NN
world0.00351929966767313NN

Label: 17, with a score of: 0.174634007066347
WordScorePoS
effect0NN
climate0NN
change0NN
city0NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

Megacities Threatened by Sea Level Rise and Storm Surges BOX Source Reprinted from World Bank

Label: 13, with a score of: 0.774207191916733
WordScorePoS
threaten0VB
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
source0.0123065238088614NN
world0.00351929966767313NN
bank0NN

Label: 2, with a score of: 0.314715471860635
WordScorePoS
threaten0VB
sea0NN
level0.00552410547653725NN
rise0.0214456704626236NN
source0.0130963126055467NN
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 6, with a score of: 0.311315901583974
WordScorePoS
threaten0VB
sea0.041835863322702NN
level0.00647010760354177NN
rise0.0251182378961834NN
source0.0613562156822703NN
world0.00438651305628668NN
bank0NN

Label: 14, with a score of: 0.304726119311491
WordScorePoS
threaten0VB
sea0.0977100948994686NN
level0.010074208709011NN
rise0NN
source0.0238835024181023NN
world0.0045533140757086NN
bank0NN

Urbanization in Coastal Areas Many of the regions that are witnessing rapid urban growth and in migration are located in coastal areas

Label: 14, with a score of: 0.651249765124439
WordScorePoS
urbanization0NN
coastal0.265991560286306JJ
region0NN
rapid0.0271683202880942JJ
urban0JJ
growth0NN
migration0NN

Label: 11, with a score of: 0.535597960443217
WordScorePoS
urbanization0.146277152173395NN
coastal0JJ
region0NN
rapid0.0373517915079663JJ
urban0.27622591898666JJ
growth0NN
migration0NN

Label: 10, with a score of: 0.337822222704928
WordScorePoS
urbanization0NN
coastal0JJ
region0NN
rapid0JJ
urban0.0488696285210061JJ
growth0.11178243319132NN
migration0.12939607756667NN

Label: 1, with a score of: 0.2751131426084
WordScorePoS
urbanization0NN
coastal0JJ
region0.184259548107002NN
rapid0JJ
urban0JJ
growth0.0519476926891779NN
migration0NN

Label: 8, with a score of: 0.242918623677293
WordScorePoS
urbanization0NN
coastal0JJ
region0NN
rapid0JJ
urban0JJ
growth0.208565600657229NN
migration0NN

This is of particular concern in the context of climate change where risk from sea level rise and increases in the frequency and intensity of extreme weather pose serious threat to the infrastructure and economic development of the city not to mention its residents

Label: 13, with a score of: 0.688339981404256
WordScorePoS
context0.0414107553022249NN
climate0.291881734282273NN
change0.402565546215053NN
risk0NN
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
increase0.0145316075945746NN
intensity0NN
extreme0.0335649154577099JJ
weather0.0671298309154199NN
pose0VB
threat0NN
infrastructure0NN
economic0JJ
development0NN
city0NN

Label: 11, with a score of: 0.484093791427696
WordScorePoS
context0NN
climate0.0229054731708393NN
change0.0315914058171786NN
risk0.0631828116343571NN
sea0NN
level0.00692515656684925NN
rise0.0268848280079943NN
increase0.00323105002784081NN
intensity0NN
extreme0JJ
weather0NN
pose0VB
threat0NN
infrastructure0.0194583992186006NN
economic0.0389167984372011JJ
development0.0100241617629471NN
city0.607697021770652NN

Label: 1, with a score of: 0.295997122287295
WordScorePoS
context0NN
climate0.0376648791150643NN
change0NN
risk0.0519476926891779NN
sea0NN
level0.0113874611101829NN
rise0NN
increase0NN
intensity0NN
extreme0.294526332970048JJ
weather0NN
pose0VB
threat0NN
infrastructure0NN
economic0.0959899298575892JJ
development0.0164833460638534NN
city0NN

Label: 9, with a score of: 0.172080002121186
WordScorePoS
context0NN
climate0NN
change0NN
risk0NN
sea0NN
level0NN
rise0.0221298700466866NN
increase0.0159575617614215NN
intensity0NN
extreme0JJ
weather0NN
pose0VB
threat0NN
infrastructure0.192202909044225NN
economic0.0320338181740375JJ
development0.0330049940546426NN
city0NN

Label: 8, with a score of: 0.0866486402639175
WordScorePoS
context0NN
climate0NN
change0NN
risk0NN
sea0NN
level0.00761994916892276NN
rise0NN
increase0.00711043475673939NN
intensity0NN
extreme0JJ
weather0NN
pose0VB
threat0NN
infrastructure0NN
economic0.128463821595706JJ
development0.0055149369084567NN
city0NN

There is tendency for private investments and enterprises to develop in coastal areas due to easy transportation access through ports

Label: 14, with a score of: 0.689532032219291
WordScorePoS
private0JJ
investment0NN
enterprise0NN
develop0VB
coastal0.265991560286306JJ
due0JJ
transportation0.0325700316331562NN
access0.00113832851892715NN

Label: 9, with a score of: 0.410447539109391
WordScorePoS
private0.0368585655748969JJ
investment0.0377086395700813NN
enterprise0.0909486002868787NN
develop0VB
coastal0JJ
due0JJ
transportation0NN
access0.0128821355880844NN

Label: 17, with a score of: 0.390300699572006
WordScorePoS
private0.0898760357768464JJ
investment0.0766240489430275NN
enterprise0NN
develop0VB
coastal0JJ
due0JJ
transportation0NN
access0.00314118376811005NN

Such economic investments further reinforce the rapid urbanization in coastal areas

Label: 14, with a score of: 0.627685503681308
WordScorePoS
economic0.0141533244035107JJ
investment0NN
reinforce0VB
rapid0.0271683202880942JJ
urbanization0NN
coastal0.265991560286306JJ

Label: 11, with a score of: 0.454548434532324
WordScorePoS
economic0.0389167984372011JJ
investment0NN
reinforce0VB
rapid0.0373517915079663JJ
urbanization0.146277152173395NN
coastal0JJ

Label: 8, with a score of: 0.313864751061445
WordScorePoS
economic0.128463821595706JJ
investment0.0252035516550537NN
reinforce0VB
rapid0JJ
urbanization0NN
coastal0JJ

While larger companies and corporations could eventually choose to relocate away from risk prone coastal areas and assume higher transpor tation costs it is difficult to conceive way in which entire coastal cities will effectively retrofit for climateresilient development

Label: 11, with a score of: 0.754058578053804
WordScorePoS
company0NN
risk0.0631828116343571NN
coastal0JJ
cost0NN
city0.607697021770652NN
effectively0RB
development0.0100241617629471NN

Label: 14, with a score of: 0.593175303481142
WordScorePoS
company0NN
risk0NN
coastal0.265991560286306JJ
cost0NN
city0NN
effectively0RB
development0.0036456034155327NN

Large sections of cities with more than million inhabitants Mumbai Shanghai and Dhaka for instance are at risk from sea level rise

Label: 11, with a score of: 0.875719716790756
WordScorePoS
city0.607697021770652NN
risk0.0631828116343571NN
sea0NN
level0.00692515656684925NN
rise0.0268848280079943NN

Label: 13, with a score of: 0.410422967796036
WordScorePoS
city0NN
risk0NN
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN

The economic success of these cities is critical for their overall nations well being both economically and culturally

Label: 11, with a score of: 0.925105716750102
WordScorePoS
economic0.0389167984372011JJ
city0.607697021770652NN
critical0JJ
nation0NN
economically0RB

Label: 8, with a score of: 0.183792260603943
WordScorePoS
economic0.128463821595706JJ
city0NN
critical0JJ
nation0NN
economically0RB

In cities such as these the exodus of private sector investment due to climate risk could have an important impact on the countries economies

Label: 11, with a score of: 0.720707469821677
WordScorePoS
city0.607697021770652NN
private0JJ
sector0NN
investment0NN
due0JJ
climate0.0229054731708393NN
risk0.0631828116343571NN
impact0.0194583992186006NN
country0NN
economy0NN

Label: 13, with a score of: 0.471880827111003
WordScorePoS
city0NN
private0JJ
sector0NN
investment0NN
due0.0473606524958885JJ
climate0.291881734282273NN
risk0NN
impact0.0437569550806737NN
country0NN
economy0.0839946104936743NN

Source Sattherthwaite et al

Label: 6, with a score of: 0.73415387760794
WordScorePoS
source0.0613562156822703NN

Case Study Blackouts in India and the United States On the east coast of the United States severe summer storms knocked out power lines through several states

Label: 0, with a score of: 1

The blackouts at the end of June lasted several days several weeks for many affecting millions of people in the capital Washington DC and spreading from North Carolina to New Jersey and as far west as Illinois

Label: 1, with a score of: 0.874251859458032
WordScorePoS
last0JJ
day0.25973846344589NN
affect0.0184786920895884VB
million0CD
people0.0371911007748996NNS
spread0NN
north0RB

A record breaking heat wave followed the next week when those without power had no way of combating the extreme temperatures

Label: 1, with a score of: 0.728429116441682
WordScorePoS
record0NN
power0NN
combate0NN
extreme0.294526332970048JJ
temperature0NN

Label: 13, with a score of: 0.595103532586458
WordScorePoS
record0NN
power0NN
combate0NN
extreme0.0335649154577099JJ
temperature0.207053776511125NN

Ultimately work crews from power utilities in Canada had to assist in the weeks long effort to restore power

Label: 0, with a score of: 1

In what has been called the biggest blackout in the world more than million people in India were without power few weeks later after the country In addition to catastrophic risks systemic climaterelated risks arise from poor urban design and construction as well as from interruptions to urban service provision and management systems

Label: 11, with a score of: 0.783735338493889
WordScorePoS
world0.00626002771693937NN
people0.0161552501392041NNS
power0NN
country0NN
addition0NN
catastrophic0JJ
risk0.0631828116343571NN
systemic0JJ
arise0VB
poor0.0229054731708393JJ
urban0.27622591898666JJ
service0.0269743071558856NN
provision0NN
management0.0687164195125178NN
system0.0315914058171786NN

Label: 1, with a score of: 0.359569481001835
WordScorePoS
world0.00257343720272548NN
people0.0371911007748996NNS
power0NN
country0NN
addition0NN
catastrophic0JJ
risk0.0519476926891779NN
systemic0JJ
arise0VB
poor0.150659516460257JJ
urban0JJ
service0.0443555132287109NN
provision0NN
management0NN
system0.0519476926891779NN

Systemic risks can lead to sustained losses due to highly inefficient systems for health care energy water and food supply arising from poor maintenance old technology and poor demand side and life cycle management

Label: 12, with a score of: 0.501742006238139
WordScorePoS
systemic0.0409791940864438JJ
risk0NN
lead0.0169905820958638NN
sustained0JJ
loss0.0468670845659596NN
due0.0234335422829798JJ
highly0RB
system0NN
health0.0339811641917276NN
care0NN
energy0.133960988253756NN
water0.186896403054502NN
food0.186896403054502NN
supply0.0703006268489394NN
arise0VB
poor0.0339811641917276JJ
technology0NN
demand0NN
life0.0250071479597636NN
cycle0.108503934593034NN
management0.0339811641917276NN

Label: 6, with a score of: 0.461630077647143
WordScorePoS
systemic0JJ
risk0NN
lead0NN
sustained0JJ
loss0NN
due0.0590310971343993JJ
highly0RB
system0NN
health0NN
care0NN
energy0.0153390539205676NN
water0.556409482163189NN
food0.0214003646985842NN
supply0.0590310971343993NN
arise0VB
poor0.0642010940957526JJ
technology0.0153390539205676NN
demand0NN
life0NN
cycle0NN
management0.0428007293971684NN

Label: 7, with a score of: 0.387140408743965
WordScorePoS
systemic0JJ
risk0NN
lead0.0383371018038178NN
sustained0JJ
loss0NN
due0JJ
highly0RB
system0NN
health0NN
care0NN
energy0.494617163780726NN
water0NN
food0.0383371018038178NN
supply0.0528748274225028NN
arise0VB
poor0JJ
technology0.109914925284606NN
demand0NN
life0.0188084899376888NN
cycle0NN
management0NN

Label: 2, with a score of: 0.309977203893524
WordScorePoS
systemic0JJ
risk0.0252000451111224NN
lead0NN
sustained0JJ
loss0NN
due0JJ
highly0RB
system0.0756001353333673NN
health0NN
care0NN
energy0.0130963126055467NN
water0NN
food0.292342270824999NN
supply0NN
arise0.0440683498519077VB
poor0.0548141757796873JJ
technology0.0130963126055467NN
demand0.0297950283197006NN
life0NN
cycle0NN
management0NN

Label: 1, with a score of: 0.258459361296381
WordScorePoS
systemic0JJ
risk0.0519476926891779NN
lead0NN
sustained0JJ
loss0NN
due0.0519476926891779JJ
highly0RB
system0.0519476926891779NN
health0NN
care0NN
energy0NN
water0NN
food0NN
supply0NN
arise0VB
poor0.150659516460257JJ
technology0.0269969049497485NN
demand0NN
life0.0184786920895884NN
cycle0NN
management0NN

Label: 3, with a score of: 0.251040521914477
WordScorePoS
systemic0JJ
risk0.0703902516159815NN
lead0.025518364538905NN
sustained0JJ
loss0NN
due0JJ
highly0RB
system0NN
health0.191387734041787NN
care0.104031537001265NN
energy0NN
water0.0382775468083574NN
food0NN
supply0NN
arise0VB
poor0JJ
technology0NN
demand0.020806307400253NN
life0.0250390290383647NN
cycle0NN
management0.0127591822694525NN

These risks have been graphically illustrated by sustained water and energy supply shortages in China India and western parts of the United States ICLEI and by blackouts in India and the eastern United States Box

Label: 6, with a score of: 0.653004606240709
WordScorePoS
risk0NN
sustained0JJ
water0.556409482163189NN
energy0.0153390539205676NN
supply0.0590310971343993NN
shortage0.0516150472983178NN
western0JJ
unite0VB
eastern0JJ

Label: 7, with a score of: 0.523912044368545
WordScorePoS
risk0NN
sustained0JJ
water0NN
energy0.494617163780726NN
supply0.0528748274225028NN
shortage0NN
western0JJ
unite0VB
eastern0JJ

Label: 12, with a score of: 0.3743112232811
WordScorePoS
risk0NN
sustained0JJ
water0.186896403054502NN
energy0.133960988253756NN
supply0.0703006268489394NN
shortage0NN
western0JJ
unite0VB
eastern0JJ

Put another way city resilience is determined not only by its exposure to hazards and the vulner BOX Resilience is not just challenge for low and middleincome countries

Label: 11, with a score of: 0.930552825253225
WordScorePoS
city0.607697021770652NN
resilience0.0373517915079663NN
determine0VB
exposure0NN
hazard0NN
challenge0.0947742174515357NN
country0NN

Within single month in both India and the United States experienced massive power outages that revealed vulnerable electricity grids

Label: 13, with a score of: 0.578870368565901
WordScorePoS
single0JJ
unite0.0828215106044499VB
experienced0.0548233071065585JJ
power0NN
vulnerable0.0171695137813101JJ
electricity0NN

electricity supply was overloaded by fast growing demand even though only percent of the Indian population has access to electricity

Label: 7, with a score of: 0.500261003916918
WordScorePoS
electricity0.0625160381065121NN
supply0.0528748274225028NN
grow0VB
demand0NN
population0NN
access0.0157161992303584NN

Label: 6, with a score of: 0.44567624104291
WordScorePoS
electricity0.0348974218717993NN
supply0.0590310971343993NN
grow0VB
demand0NN
population0.0363595928905615NN
access0.00731085509381114NN

Label: 2, with a score of: 0.377709354560734
WordScorePoS
electricity0.0297950283197006NN
supply0NN
grow0.0182713919265625VB
demand0.0297950283197006NN
population0.0310434135749871NN
access0.00749031153171887NN

Label: 10, with a score of: 0.330640778817807
WordScorePoS
electricity0NN
supply0NN
grow0.0405241466500776VB
demand0NN
population0.0860640399817412NN
access0.00138439773401932NN

Label: 12, with a score of: 0.312721901950453
WordScorePoS
electricity0NN
supply0.0703006268489394NN
grow0.0339811641917276VB
demand0NN
population0.0144336476662975NN
access0.00232174891244877NN

a Three of the country five electricity grids four of which are interconnected failed simultaneously causing outages in of India states along with the capital New Delhi

Label: 0, with a score of: 1

This led to cascading failures along thousands of miles of the commuter train system

Label: 0, with a score of: 1

It also affected the operation of hospitals and other crucial urban systems such as traffic lights

Label: 11, with a score of: 0.89947340727869
WordScorePoS
affect0.0112376090361851VB
crucial0JJ
urban0.27622591898666JJ
system0.0315914058171786NN

Such cascading failures indicate significant lack of resilience in urban systems

Label: 11, with a score of: 0.845872687207792
WordScorePoS
lack0.0229054731708393NN
resilience0.0373517915079663NN
urban0.27622591898666JJ
system0.0315914058171786NN

Label: 1, with a score of: 0.433645508341326
WordScorePoS
lack0.0753297582301286NN
resilience0.0614198493690006NN
urban0JJ
system0.0519476926891779NN

In both rapidly developing countries and the world largest economy energy infrastructure needs to be diversified from generation to transmission to distribution

Label: 7, with a score of: 0.781402440703192
WordScorePoS
rapidly0RB
develop0VB
country0NN
world0.00261936653839306NN
largest0JJS
economy0.0625160381065121NN
energy0.494617163780726NN
infrastructure0.0651354046449052NN
generation0NN

Label: 9, with a score of: 0.470980090014312
WordScorePoS
rapidly0RB
develop0VB
country0NN
world0.00257642711761688NN
largest0.0368585655748969JJS
economy0NN
energy0.0540565419125769NN
infrastructure0.192202909044225NN
generation0.0909486002868787NN

Label: 12, with a score of: 0.311904455994474
WordScorePoS
rapidly0RB
develop0VB
country0NN
world0.00464349782489754NN
largest0JJS
economy0NN
energy0.133960988253756NN
infrastructure0.028867295332595NN
generation0.0819583881728876NN

We must address vulnerable links such as power grids if we are to bring about more sustainable cities

Label: 11, with a score of: 0.97405883525127
WordScorePoS
vulnerable0.0458109463416786JJ
power0NN
bring0.0731385760866977VB
sustainable0.00782503464617421JJ
city0.607697021770652NN

http data

Label: 17, with a score of: 0.851744941703599
WordScorePoS
datum0.0739231123597559NN

Label: 9, with a score of: 0.523956633971108
WordScorePoS
datum0.0454743001434393NN

worldbank

Label: 0, with a score of: 1

org indicator EG

Label: 0, with a score of: 1

ELC

Label: 0, with a score of: 1

ACCS

Label: 0, with a score of: 1

ZS ability of its population and assets but by its institutional and community capacity to respond to stress

Label: 10, with a score of: 0.477572141214215
WordScorePoS
ability0NN
population0.0860640399817412NN
asset0.0488696285210061NN
institutional0JJ
community0.0202620733250388NN
capacity0NN
stress0NN

Label: 13, with a score of: 0.438292294271427
WordScorePoS
ability0NN
population0NN
asset0NN
institutional0.0828215106044499JJ
community0.0343390275626203NN
capacity0.025270503924109NN
stress0NN

Label: 12, with a score of: 0.415397944031902
WordScorePoS
ability0.0409791940864438NN
population0.0144336476662975NN
asset0NN
institutional0JJ
community0.0169905820958638NN
capacity0.00833571598658786NN
stress0.0542519672965169NN

Label: 8, with a score of: 0.356878666864736
WordScorePoS
ability0.0607878664258118NN
population0.0428212738652355NN
asset0NN
institutional0JJ
community0NN
capacity0.0123650647908625NN
stress0NN

Label: 16, with a score of: 0.300672241067038
WordScorePoS
ability0NN
population0NN
asset0.0811931365363091NN
institutional0JJ
community0NN
capacity0.016515769558553NN
stress0NN

A paradigm shift is necessary to give equal emphasis to capacities as opposed to the traditional technical approach of focusing on exposure to hazards

Label: 4, with a score of: 0.485393628790671
WordScorePoS
equal0.051052537468787JJ
capacity0NN
traditional0JJ
technical0.124876512346911JJ
approach0NN
focus0NN
exposure0NN
hazard0NN

Label: 1, with a score of: 0.435736846982494
WordScorePoS
equal0.0376648791150643JJ
capacity0NN
traditional0JJ
technical0JJ
approach0NN
focus0NN
exposure0.120266261535276NN
hazard0NN

Label: 8, with a score of: 0.399978516121214
WordScorePoS
equal0.0504071033101074JJ
capacity0.0123650647908625NN
traditional0JJ
technical0.0410992516792148JJ
approach0NN
focus0.0410992516792148NN
exposure0NN
hazard0NN

The city network ICLEI looks at climate change related risks as subset of larger pool of catastrophic risks confronting the world growing cities and urbanizing countries recently exemplified by the Indian Ocean tsunami and the Tohoku earthquake in Japan

Label: 11, with a score of: 0.794476423433551
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN
related0.0112376090361851JJ
risk0.0631828116343571NN
catastrophic0JJ
world0.00626002771693937NN
grow0.0229054731708393VB
country0NN
ocean0NN

Label: 13, with a score of: 0.460472953279465
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN
related0.0168470026160727JJ
risk0NN
catastrophic0JJ
world0.00351929966767313NN
grow0.0171695137813101VB
country0NN
ocean0.10069474637313NN

Label: 14, with a score of: 0.286421141091454
WordScorePoS
city0NN
climate0.0166605993001162NN
change0NN
related0.0081738237776987JJ
risk0NN
catastrophic0JJ
world0.0045533140757086NN
grow0VB
country0NN
ocean0.488550474497343NN

Other disasters are the result of systemic risks directly related to the enormous resource demands of growing cities that altogether account for some percent of global energy demand

Label: 11, with a score of: 0.768810729858622
WordScorePoS
disaster0.157957029085893NN
result0NN
systemic0JJ
risk0.0631828116343571NN
related0.0112376090361851JJ
resource0.0449504361447405NN
demand0NN
grow0.0229054731708393VB
city0.607697021770652NN
account0.0194583992186006NN
global0.00501208088147356JJ
energy0.0492535935240667NN

Label: 7, with a score of: 0.407083267281363
WordScorePoS
disaster0NN
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grow0VB
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global0.0251662980592747JJ
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Label: 12, with a score of: 0.265479589114906
WordScorePoS
disaster0NN
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Trillions of dollars are being invested annually in global BUILDING SUSTAINABILITY IN AN URBANIZING WORLD urban development typically to design and build cities that embody chronic systemic risk and often extreme catastrophic risk

Label: 11, with a score of: 0.823681014933942
WordScorePoS
trillion0CD
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Label: 1, with a score of: 0.356561158539971
WordScorePoS
trillion0CD
dollar0NN
invest0VB
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world0.00257343720272548NN
urban0JJ
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typically0RB
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risk0.0519476926891779NN
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Label: 17, with a score of: 0.293373712434986
WordScorePoS
trillion0CD
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invest0VB
annually0RB
global0.0201198688839505JJ
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typically0RB
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This is true even in affluent regions such as Japan California Silicon Valley Vancouver coastal plains or the low lying and hurricane prone southern Florida area

Label: 14, with a score of: 0.702224248330584
WordScorePoS
region0NN
coastal0.265991560286306JJ
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Label: 1, with a score of: 0.540499699931577
WordScorePoS
region0.184259548107002NN
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lie0NN
southern0.0614198493690006JJ

Label: 3, with a score of: 0.366194415064677
WordScorePoS
region0.124837844401518NN
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Adaptation Planning at the City Level BOX Adaptation to climate change can be defined as the set of organization location and technical changes that societies will have to implement to limit the negative effects of climate change and to maximize the beneficial ones Hallegatte et al

Label: 13, with a score of: 0.802838527010481
WordScorePoS
adaptation0.0671298309154199NN
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Label: 11, with a score of: 0.458997655055928
WordScorePoS
adaptation0.04477822029736NN
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Examples of adaptation actions include removing populations and assets from areas at risk protecting infrastructure adjusting energy networks to accommodate variations in energy consumption and climate and preparing emergency response plans for extreme weather events Box

Label: 7, with a score of: 0.661712525280458
WordScorePoS
adaptation0NN
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Label: 12, with a score of: 0.416712650284778
WordScorePoS
adaptation0NN
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climate0NN
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Label: 13, with a score of: 0.365913775383277
WordScorePoS
adaptation0.0671298309154199NN
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weather0.0671298309154199NN
event0.0414107553022249NN

Label: 1, with a score of: 0.284052581517667
WordScorePoS
adaptation0NN
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event0.0908430554521382NN

Label: 9, with a score of: 0.177585679994412
WordScorePoS
adaptation0NN
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Label: 8, with a score of: 0.118095372894368
WordScorePoS
adaptation0NN
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Climate change adaptation requires effective collaboration across sectors and between multiple stakeholders Box

Label: 13, with a score of: 0.891913385719353
WordScorePoS
climate0.291881734282273NN
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Cities are well positioned to convene wide range of partners such as government agencies local communities nonprofit organizations academic institutions and the private sector

Label: 11, with a score of: 0.773566252316579
WordScorePoS
city0.607697021770652NN
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Label: 16, with a score of: 0.338554562910362
WordScorePoS
city0NN
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institution0.274477239401321NN
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Label: 17, with a score of: 0.326944188235053
WordScorePoS
city0NN
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Under uncertain climate conditions partnerships are key to dynamic adaptation process that will allow cities to prepare respond and continue on their path toward sustainable development World Bank

Label: 11, with a score of: 0.695139542425728
WordScorePoS
climate0.0229054731708393NN
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Label: 17, with a score of: 0.453187622361373
WordScorePoS
climate0NN
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Label: 13, with a score of: 0.371753577295915
WordScorePoS
climate0.291881734282273NN
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Chapter discusses the roles that different actors can take in such process

Label: 9, with a score of: 0.659440698120996
WordScorePoS
actor0NN
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Keeping Cities Safe in Extreme Weather Provision of basic services in cities is key aspect of adaptation practice

Label: 11, with a score of: 0.917881891756052
WordScorePoS
city0.607697021770652NN
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Label: 1, with a score of: 0.257310245981718
WordScorePoS
city0NN
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In many cities adaptation activities are already being mainstreamed in emergency planning to keep citizens safe and businesses operating in case of an extreme weather event

Label: 11, with a score of: 0.822624896144631
WordScorePoS
city0.607697021770652NN
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Label: 1, with a score of: 0.39504720639673
WordScorePoS
city0NN
adaptation0NN
activity0NN
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extreme0.294526332970048JJ
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In Yokohama agreements are in place with private companies to provide the city with food in case of disruption to supply

Label: 11, with a score of: 0.740968699906581
WordScorePoS
agreement0NN
private0JJ
company0NN
provide0.0269743071558856VB
city0.607697021770652NN
food0NN
supply0NN

Label: 12, with a score of: 0.450309931809429
WordScorePoS
agreement0NN
private0JJ
company0.108503934593034NN
provide0.0200087191734851VB
city0NN
food0.186896403054502NN
supply0.0703006268489394NN

Label: 2, with a score of: 0.417874868986201
WordScorePoS
agreement0NN
private0JJ
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provide0.0215170467912497VB
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Seattle has created Vulnerable Populations Action Team Community Communication Network

Label: 1, with a score of: 0.47856135328044
WordScorePoS
create0.0519476926891779VB
vulnerable0.112994637345193JJ
population0NN
action0.0269969049497485NN
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communication0NN

Label: 9, with a score of: 0.440349300055653
WordScorePoS
create0.0520080474473126VB
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population0NN
action0.0135141354781442NN
community0.0188543197850406NN
communication0.0922368138456871NN

Label: 10, with a score of: 0.351832388921825
WordScorePoS
create0VB
vulnerable0.0202620733250388JJ
population0.0860640399817412NN
action0.0145231653596925NN
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This partnership between public health organizations community based organizations and community leaders ensures that important health information reaches vulnerable populations in the event of an emergency

Label: 17, with a score of: 0.552448525454137
WordScorePoS
partnership0.3914644359026NN
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information0.0219686053934784NN
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Label: 3, with a score of: 0.46713549781269
WordScorePoS
partnership0NN
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Label: 10, with a score of: 0.306581780648068
WordScorePoS
partnership0NN
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organization0.02794560829783NN
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In Chicago the city maintains an Extreme Weather Operations Plan that prescribes actions during times of extreme heat cold or severe storms

Label: 11, with a score of: 0.695643450642947
WordScorePoS
city0.607697021770652NN
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Label: 1, with a score of: 0.594122431187152
WordScorePoS
city0NN
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Label: 2, with a score of: 0.317358716418003
WordScorePoS
city0.0440683498519077NN
maintain0.0881366997038154VB
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As part of the city adaptation activities the Department of Aviation Action is working to improve service for stranded passengers in the event of storms or extreme weather

Label: 11, with a score of: 0.754982163399075
WordScorePoS
city0.607697021770652NN
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Label: 1, with a score of: 0.487824822267164
WordScorePoS
city0NN
adaptation0NN
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extreme0.294526332970048JJ
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New York has several emergency and disaster response plans including Natural Hazard Mitigation Plan Coastal Storm Plan Citywide Debris Management Plan Power Disruption Plan and Flash Flood Emergency Plan

Label: 11, with a score of: 0.655152421905474
WordScorePoS
disaster0.157957029085893NN
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management0.0687164195125178NN
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Label: 14, with a score of: 0.454145082686883
WordScorePoS
disaster0NN
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Label: 13, with a score of: 0.425565400316848
WordScorePoS
disaster0.0236803262479443NN
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Johannesburg has developed heat wave response plan and many cities have programs to educate the public about how to prevent heatstroke and what to do in the event of heat wave

Label: 11, with a score of: 0.942963028604861
WordScorePoS
develop0VB
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A number of cities designate cooling centers for use by residents during extreme heat events

Label: 11, with a score of: 0.829456620675781
WordScorePoS
city0.607697021770652NN
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Label: 1, with a score of: 0.525997625759627
WordScorePoS
city0NN
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Urbanization processes are complex involving many stakeholders and incentives for growth and development can end up putting more people in the path of environmental hazard

Label: 8, with a score of: 0.447532253735419
WordScorePoS
urbanization0NN
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Label: 11, with a score of: 0.398081983733652
WordScorePoS
urbanization0.146277152173395NN
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environmental0.0537696560159885JJ
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Label: 12, with a score of: 0.381943630258968
WordScorePoS
urbanization0NN
process0NN
involve0.0819583881728876VB
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Label: 10, with a score of: 0.365808049373118
WordScorePoS
urbanization0NN
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Label: 9, with a score of: 0.30525438363023
WordScorePoS
urbanization0NN
process0.0737171311497939NN
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hazard0NN

An example is Altos de Cazucá district located in the mountain slopes of the Colombian Municipality of Soacha the most densely populated municipality in the Department of Cundinamarca which includes the capital Bogotá

Label: 0, with a score of: 1

Soacha has total population of approximately half million people accelerated population growth is mainly due to economic opportunities in the municipality and in neighboring Bogotá as well as internal displacement due to armed conflict AINCA p

Label: 8, with a score of: 0.529711704826896
WordScorePoS
total0NN
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people0.0106656521351091NNS
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economic0.128463821595706JJ
opportunity0.0361300980868102NN
arm0NN

Label: 10, with a score of: 0.476096359312484
WordScorePoS
total0NN
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approximately0RB
people0.00571634317201755NNS
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growth0.11178243319132NN
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economic0.103276847978089JJ
opportunity0.029046330719385NN
arm0NN

Label: 1, with a score of: 0.461812155459271
WordScorePoS
total0NN
population0NN
approximately0RB
people0.0371911007748996NNS
accelerate0.120266261535276VB
growth0.0519476926891779NN
due0.0519476926891779JJ
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opportunity0NN
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Photo Julianne Baker Gallegos World Bank The majority of properties in Altos de Cazucá are not titled or legally registered and only percent of the population has access to basic services such as drinking water sewers streets or health services OCHA

Label: 6, with a score of: 0.769107976139595
WordScorePoS
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health0NN

Label: 3, with a score of: 0.256048843204068
WordScorePoS
world0.00087176582269799NN
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water0.0382775468083574NN
health0.191387734041787NN

Label: 12, with a score of: 0.24553157235857
WordScorePoS
world0.00464349782489754NN
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majority0NN
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drinking0NN
water0.186896403054502NN
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Industrial activities are the main source of income in the municipality and companies also facilitate development by providing certain services such as electricity to support company employees who live in the area AINCA p

Label: 9, with a score of: 0.665368652501883
WordScorePoS
industrial0.318320101004075JJ
activity0NN
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company0NN
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electricity0.030745604615229NN
support0.0277502419802315NN
live0.00740117451847634VB

Label: 10, with a score of: 0.266350243017175
WordScorePoS
industrial0JJ
activity0NN
source0NN
income0.166475342664294NN
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facilitate0.02794560829783VB
development0.0177346522508396NN
provide0VB
service0.00795378154685591NN
electricity0NN
support0NN
live0.0159075630937118VB

Label: 12, with a score of: 0.332091290820995
WordScorePoS
industrial0JJ
activity0.0199423462679015NN
source0NN
income0NN
company0.108503934593034NN
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development0.022306853314744NN
provide0.0200087191734851VB
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One of the largest industries is mining the mountains here are an important source of construction materials for Bogotá

Label: 9, with a score of: 0.692160561137533
WordScorePoS
largest0.0368585655748969JJS
industry0.181897200573757NN
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source0.0135141354781442NN
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Label: 6, with a score of: 0.490464550423092
WordScorePoS
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industry0NN
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material0.0516150472983178NN

Label: 15, with a score of: 0.30211247596207
WordScorePoS
largest0JJS
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BOX Case Study Growing Risks and Multiple Stakeholders in Altos de Cazucá These mountains had been quarried in an unregulated manner for decades before they became populated causing slope instability

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WordScorePoS
study0.0517339898971162NN
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Label: 11, with a score of: 0.465305220418917
WordScorePoS
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Recurring small scale landslides affect local livelihoods and as the mountain slopes fill with people more are harmed by these events

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WordScorePoS
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Label: 6, with a score of: 0.487732382877072
WordScorePoS
recur0.0683326727248364VB
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Label: 1, with a score of: 0.454885163806377
WordScorePoS
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According to several risk assessments more than neighborhoods in Altos de Cazucá cannot be legalized due to potential geohazards

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WordScorePoS
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Two different geologic risk assessments performed by the Ministerio de Planeación de Colombia Urban Planning Ministry of Colombia and the Japanese International Cooperation Agency have already determined that certain areas within the district are uninhabitable JICA

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WordScorePoS
risk0.0631828116343571NN
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While the private sector has supported the local population through service provision the local government and other multilateral and nongovernmental institutions have grappled with relocation and other measures to make residents less vulnerable

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WordScorePoS
private0JJ
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Label: 17, with a score of: 0.432598568405952
WordScorePoS
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Label: 1, with a score of: 0.366853458487434
WordScorePoS
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Label: 8, with a score of: 0.303245578212896
WordScorePoS
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The case of Altos de Cazucá shows that in rapidly urbanizing area where different stakeholders are trying to support development it is important to map risk and coordinate at multiple levels to build resilience BakerGallegos

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Label: 1, with a score of: 0.460317552865689
WordScorePoS
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Most of the costs of adaptation will be borne by cities

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WordScorePoS
building0.0747035830159326NN
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Label: 17, with a score of: 0.239173032922207
WordScorePoS
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Cost estimates are wide ranging but the UNFCCC estimates global cost of billion per year by Parry et al

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WordScorePoS
cost0NN
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Label: 13, with a score of: 0.466203735590203
WordScorePoS
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and the World Bank estimates billion per year

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WordScorePoS
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Label: 3, with a score of: 0.4426965963038
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Currently adaptation is financed mainly through private income national and municipal revenues grants from multilateral and bilateral institutions and market based mechanisms

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WordScorePoS
adaptation0NN
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WordScorePoS
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Label: 10, with a score of: 0.394798542441306
WordScorePoS
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There is ample room for cities to be creative in leveraging more funding from donors and collaborating with the private sector to help finance adaptation World Bank

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WordScorePoS
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Public policies dealing with adaptation have four pillars information changing standards and regulations improving institutions and changing investment decisions

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The first step in building adaptive capacity in city is focused on information

Label: 11, with a score of: 0.913991200350426
WordScorePoS
building0.0747035830159326NN
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Label: 17, with a score of: 0.271350068965084
WordScorePoS
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In many cases cities can gather data and map the communities assets and services that are vulnerable to climate related risks and have clear understanding of how these could be strengthened to better confront impending climate change impacts

Label: 13, with a score of: 0.707060143392986
WordScorePoS
city0NN
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Label: 11, with a score of: 0.556436254006482
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Providing all stakeholders with the best information available will help them handle the uncertainty around projected climate impacts Box

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provide0VB
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Label: 12, with a score of: 0.482424371816378
WordScorePoS
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Adaptation can be reactive after disaster or preventive before disaster

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WordScorePoS
adaptation0.04477822029736NN
disaster0.157957029085893NN

Label: 2, with a score of: 0.316056058313902
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Politically it is much easier to channel the necessary funds to adaptation after the fact

Label: 13, with a score of: 0.714739150020976
WordScorePoS
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Label: 11, with a score of: 0.658653553625482
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However in economic and social terms it makes more sense to act preventively as the costs to economic assets and the existing social network are substantially lower World Bank

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Label: 17, with a score of: 0.258494692210109
WordScorePoS
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Label: 10, with a score of: 0.650412609220068
WordScorePoS
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Label: 1, with a score of: 0.433340443621458
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Often it is the responsibility of national or supranational agencies to produce knowledge relevant to impact assessment and adaptation but local governments still play an important role in establishing early warning systems for city residents

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A classic example is Havana where very effective early warning system ensures low degree of damage when the city is hit by storms and tornados

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Label: 13, with a score of: 0.2948537830022
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BOX Incorporating Uncertainty in Adaptation Strategies One of the difficulties in developing adaptation strategies is dealing with uncertainty

Label: 0, with a score of: 1

This uncertainty results from three components uncertainty about the global scenario of climate change for example scenario with an average temperature increase of °C or one of °C how the global scenarios will translate at the local level la and uncertainty about the reaction of major cycles for example water and ecosystems to global and local climate changes

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The most effective method to take uncertainty into account is to ensure that stakeholders have the best information possible on the impacts of climate change and to encourage approaches that maintain flexibility for future action as additional information becomes available

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However adaptation strategies are complex because of the dynamic nature of adaptation we do not adapt in linear way going directly from one point to another

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nature0.162755901889551NN

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We move from where we are now to moving target perpetually changing climate

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For example building constructed in with lifespan of years should be adapted to the current climate as well as the climate in which will probably be very different from today climate

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building0.149940347231585NN
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Label: 7, with a score of: 0.337654240378727
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The combination of uncertainty and long asset lifespan leads to the risk of maladaptation

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Label: 16, with a score of: 0.451791610304282
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Source Reprinted from Hallegate et al

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a For example even for given amount of global warming measured as change in global mean temperature climate models diverge on the way in which climate change will affect the frequency and intensity of storm events in northern Europe

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PART II THE PATH TO SUSTAINABILITY As knowledge on new risks is obtained the next step is to change standards and regulations

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If long term forward thinking policies are put in place the costs of adjusting to climate change can be spread through time making the adaptation process affordable

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Furthermore the process of reducing climate change risks can be designed to mitigate emissions and reduce other environmental risks at the same time

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During this process local institutions have an additional role in balancing the interests of the different stakeholders for example in the case of water scarcity or public private partnerships

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partnership0NN

The fourth pillar of public action deals with the adaptation of existing public infrastructure as well as public buildings in general

Label: 17, with a score of: 0.500745942847054
WordScorePoS
public0.0390556786919513NN
action0.0109843026967392NN
adaptation0NN
exist0.0599173571845643VB
infrastructure0.0130185595639838NN
building0.149940347231585NN

Label: 9, with a score of: 0.362003389902649
WordScorePoS
public0.0160169090870187NN
action0.0135141354781442NN
adaptation0NN
exist0VB
infrastructure0.192202909044225NN
building0NN

Label: 11, with a score of: 0.511896168893873
WordScorePoS
public0.0583751976558017NN
action0NN
adaptation0.04477822029736NN
exist0.04477822029736VB
infrastructure0.0194583992186006NN
building0.0747035830159326NN

Label: 5, with a score of: 0.404404469332095
WordScorePoS
public0.0727679293221751NN
action0.0409315617631NN
adaptation0NN
exist0VB
infrastructure0.024255976440725NN
building0NN

New investments in infrastructure and buildings must also be adapted in terms of size and location for example and should be complemented by policies that restructure land use including major investments such as transportation networks regional economic development projects and so on

Label: 17, with a score of: 0.648128215051151
WordScorePoS
investment0.0766240489430275NN
infrastructure0.0130185595639838NN
building0.149940347231585NN
term0.149793392961411NN
size0NN
location0.048933054487825NN
complement0.0978661089756501NN
policy0.0641521854453136NN
land0NN
include0VB
major0JJ
transportation0NN
regional0.0422721924574123JJ
economic0JJ
development0.0469463607292177NN
project0NN

Label: 9, with a score of: 0.357671879322614
WordScorePoS
investment0.0377086395700813NN
infrastructure0.192202909044225NN
building0NN
term0NN
size0.0368585655748969NN
location0NN
complement0NN
policy0.011275328195446NN
land0NN
include0VB
major0.0160169090870187JJ
transportation0NN
regional0.0260040237236563JJ
economic0.0320338181740375JJ
development0.0330049940546426NN
project0NN

Label: 8, with a score of: 0.254020125220885
WordScorePoS
investment0.0252035516550537NN
infrastructure0NN
building0NN
term0NN
size0.049270765107907NN
location0NN
complement0NN
policy0.0452169560888236NN
land0NN
include0VB
major0.0214106369326177JJ
transportation0NN
regional0JJ
economic0.128463821595706JJ
development0.0055149369084567NN
project0NN

Increasingly cities are recognizing the need to plan for climate change by developing stand alone climate plans or integrating them into existing plans and policies

Label: 13, with a score of: 0.700457120574237
WordScorePoS
city0NN
recognize0VB
plan0.0291713033871158NN
climate0.291881734282273NN
change0.402565546215053NN
develop0VB
stand0VB
integrate0.0201523636533764VB
exist0VB
policy0.0102677744436108NN

Label: 11, with a score of: 0.616858571977532
WordScorePoS
city0.607697021770652NN
recognize0VB
plan0.0583751976558017NN
climate0.0229054731708393NN
change0.0315914058171786NN
develop0VB
stand0VB
integrate0.0537696560159885VB
exist0.04477822029736VB
policy0.0136980135278128NN

CDP Cities Global Report on Cities CDP evaluated existing climate change adaptation plans showing that about cities have such plans

Label: 11, with a score of: 0.907033772408509
WordScorePoS
city0.607697021770652NN
global0.00501208088147356JJ
report0NN
exist0.04477822029736VB
climate0.0229054731708393NN
change0.0315914058171786NN
adaptation0.04477822029736NN
plan0.0583751976558017NN

Label: 13, with a score of: 0.372334355109526
WordScorePoS
city0NN
global0.0375696197697663JJ
report0NN
exist0VB
climate0.291881734282273NN
change0.402565546215053NN
adaptation0.0671298309154199NN
plan0.0291713033871158NN

Interestingly all cities in Africa and East Asia reported that they have climate change action plans compared with less than half of cities in Latin America

Label: 11, with a score of: 0.834808151249138
WordScorePoS
city0.607697021770652NN
africa0IN
asia0IN
report0NN
climate0.0229054731708393NN
change0.0315914058171786NN
action0NN
plan0.0583751976558017NN
compare0VB

Label: 13, with a score of: 0.504020600459193
WordScorePoS
city0NN
africa0IN
asia0IN
report0NN
climate0.291881734282273NN
change0.402565546215053NN
action0.0369195714265843NN
plan0.0291713033871158NN
compare0.0414107553022249VB

Protecting Urban Water Supplies The water sector provides good example of complex climate impacts and the need for new plans and tools to manage urban resources

Label: 6, with a score of: 0.687883303186068
WordScorePoS
protect0.0153390539205676VB
urban0JJ
water0.556409482163189NN
supplies0NNS
sector0.0214003646985842NN
climate0NN
impact0.0181797964452808NN
plan0NN
tool0NN
manage0VB
resource0.0104991907358033NN

Label: 11, with a score of: 0.522953111797591
WordScorePoS
protect0.0328357290160445VB
urban0.27622591898666JJ
water0.0458109463416786NN
supplies0.0731385760866977NNS
sector0NN
climate0.0229054731708393NN
impact0.0194583992186006NN
plan0.0583751976558017NN
tool0NN
manage0VB
resource0.0449504361447405NN

Label: 12, with a score of: 0.35025223817261
WordScorePoS
protect0.0121782716594324VB
urban0JJ
water0.186896403054502NN
supplies0NNS
sector0.0169905820958638NN
climate0NN
impact0.0866018859977851NN
plan0.0144336476662975NN
tool0.0542519672965169NN
manage0VB
resource0.0416785799329393NN

Label: 13, with a score of: 0.212984844319809
WordScorePoS
protect0VB
urban0JJ
water0NN
supplies0NNS
sector0NN
climate0.291881734282273NN
impact0.0437569550806737NN
plan0.0291713033871158NN
tool0NN
manage0VB
resource0NN

Climate change can affect water availability through well known scenarios such as warmer and shorter winter seasons increased glacial melting more precipitation changes in the recharge of groundwater aquifers and warmer and potentially drier summer seasons AMWA

Label: 13, with a score of: 0.712963540520911
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
water0NN
availability0NN
scenario0.0548233071065585NN
warmer0.0548233071065585JJR
increase0.0145316075945746NN
recharge0VB
aquifer0NN
potentially0RB

Label: 6, with a score of: 0.628900668465167
WordScorePoS
climate0NN
change0NN
water0.556409482163189NN
availability0NN
scenario0NN
warmer0JJR
increase0.00905621749519576NN
recharge0.0683326727248364VB
aquifer0.136665345449673NN
potentially0RB

Label: 12, with a score of: 0.160381859596766
WordScorePoS
climate0NN
change0NN
water0.186896403054502NN
availability0NN
scenario0NN
warmer0JJR
increase0.00958677785775002NN
recharge0VB
aquifer0NN
potentially0RB

These changes will lead to shortages of water and or to higher probability of floods

Label: 6, with a score of: 0.921283253001039
WordScorePoS
lead0NN
shortage0.0516150472983178NN
water0.556409482163189NN

Label: 12, with a score of: 0.308931064163251
WordScorePoS
lead0.0169905820958638NN
shortage0NN
water0.186896403054502NN

There are indirect impacts as well

Label: 12, with a score of: 0.7540481934975
WordScorePoS
impact0.0866018859977851NN

Label: 13, with a score of: 0.380994623285423
WordScorePoS
impact0.0437569550806737NN

Label: 14, with a score of: 0.369701718692048
WordScorePoS
impact0.042459973210532NN

Severe storms and flooding will lead to water pollution from other sources including wastewater treatment and storage systems

Label: 6, with a score of: 0.92245630159259
WordScorePoS
lead0NN
water0.556409482163189NN
pollution0.0590310971343993NN
source0.0613562156822703NN
include0VB
wastewater0.204998018174509NN
treatment0.0348974218717993NN
system0NN

Label: 12, with a score of: 0.228749895548373
WordScorePoS
lead0.0169905820958638NN
water0.186896403054502NN
pollution0.0234335422829798NN
source0NN
include0VB
wastewater0NN
treatment0NN
system0NN

In most countries wastewater treatment plants have not been designed for the likely changes in flow conditions due to climate change

Label: 13, with a score of: 0.813327402730421
WordScorePoS
country0NN
wastewater0NN
treatment0NN
plant0NN
condition0NN
due0.0473606524958885JJ
climate0.291881734282273NN
change0.402565546215053NN

Label: 6, with a score of: 0.32774675664544
WordScorePoS
country0NN
wastewater0.204998018174509NN
treatment0.0348974218717993NN
plant0NN
condition0NN
due0.0590310971343993JJ
climate0NN
change0NN

Label: 2, with a score of: 0.267906138422731
WordScorePoS
country0NN
wastewater0NN
treatment0NN
plant0.132205049555723NN
condition0NN
due0JJ
climate0.0365427838531249NN
change0.0756001353333673NN

As result it is conceivable that water suppliers will face challenges from sewage overflows resulting in high concentrations of unhealthy bacteria

Label: 6, with a score of: 0.912722252249495
WordScorePoS
result0.0683326727248364NN
water0.556409482163189NN
challenge0NN
sewage0NN
concentration0NN

Label: 12, with a score of: 0.246127112307301
WordScorePoS
result0NN
water0.186896403054502NN
challenge0NN
sewage0NN
concentration0NN

More than percent of cities report that they foresee substantial risks to their water supply in the future

Label: 11, with a score of: 0.704934657032585
WordScorePoS
city0.607697021770652NN
report0NN
substantial0JJ
risk0.0631828116343571NN
water0.0458109463416786NN
supply0NN
future0.0373517915079663NN

Label: 6, with a score of: 0.575359283719491
WordScorePoS
city0NN
report0NN
substantial0JJ
risk0NN
water0.556409482163189NN
supply0.0590310971343993NN
future0NN

Label: 12, with a score of: 0.335711097754025
WordScorePoS
city0NN
report0.0409791940864438NN
substantial0.0332151194779746JJ
risk0NN
water0.186896403054502NN
supply0.0703006268489394NN
future0.0277064208763707NN

The two most common risks are increased water stress or scarcity and declining water quality CDP

Label: 6, with a score of: 0.911006912397599
WordScorePoS
common0JJ
risk0NN
increase0.00905621749519576NN
water0.556409482163189NN
stress0NN
scarcity0.273330690899345NN
decline0NN
quality0.0255958462813622NN

Label: 12, with a score of: 0.308661330530501
WordScorePoS
common0JJ
risk0NN
increase0.00958677785775002NN
water0.186896403054502NN
stress0.0542519672965169NN
scarcity0NN
decline0.0234335422829798NN
quality0.0203215381458133NN

Besides the possible reduction of water availability and quality local authorities need to anticipate shifts in demand for water

Label: 6, with a score of: 0.9098337797737
WordScorePoS
reduction0NN
water0.556409482163189NN
availability0NN
quality0.0255958462813622NN
local0.0181797964452808JJ
demand0NN

Label: 12, with a score of: 0.337071417775642
WordScorePoS
reduction0.0199423462679015NN
water0.186896403054502NN
availability0NN
quality0.0203215381458133NN
local0.0144336476662975JJ
demand0NN

However the water sector often suffers from insufficient capacity to solve technical and management problems as well as lack of cross sectoral coordination and financing difficulties

Label: 6, with a score of: 0.806226753629753
WordScorePoS
water0.556409482163189NN
sector0.0214003646985842NN
capacity0.0104991907358033NN
technical0JJ
management0.0428007293971684NN
lack0.0214003646985842NN
coordination0NN

Label: 12, with a score of: 0.304203924864618
WordScorePoS
water0.186896403054502NN
sector0.0169905820958638NN
capacity0.00833571598658786NN
technical0JJ
management0.0339811641917276NN
lack0NN
coordination0NN

Many water utilities have begun to respond to climate change by trying to understand how current plans could be disrupted and how to modify them

Label: 13, with a score of: 0.760829902719317
WordScorePoS
water0NN
climate0.291881734282273NN
change0.402565546215053NN
current0.0335649154577099JJ
plan0.0291713033871158NN
disrupt0.0548233071065585VB

Label: 6, with a score of: 0.521341654804299
WordScorePoS
water0.556409482163189NN
climate0NN
change0NN
current0JJ
plan0NN
disrupt0VB

Label: 12, with a score of: 0.219762910370248
WordScorePoS
water0.186896403054502NN
climate0NN
change0NN
current0.0332151194779746JJ
plan0.0144336476662975NN
disrupt0VB

These efforts include vulnerability analyses to identify where impacts could be felt the soonest and integrated resource planning IRP that looks at all possible alternatives for coping with systemic changes over the longer term

Label: 17, with a score of: 0.487096001400888
WordScorePoS
include0VB
vulnerability0NN
identify0VB
impact0NN
integrate0VB
resource0.0375923735942499NN
plan0.0130185595639838NN
alternative0NN
systemic0.036961556179878JJ
term0.149793392961411NN

Label: 12, with a score of: 0.41787855871048
WordScorePoS
include0VB
vulnerability0NN
identify0VB
impact0.0866018859977851NN
integrate0.0199423462679015VB
resource0.0416785799329393NN
plan0.0144336476662975NN
alternative0NN
systemic0.0409791940864438JJ
term0NN

Label: 1, with a score of: 0.3984768252825
WordScorePoS
include0VB
vulnerability0.120266261535276NN
identify0VB
impact0NN
integrate0VB
resource0.0739147683583536NN
plan0NN
alternative0NN
systemic0JJ
term0NN

Label: 11, with a score of: 0.362303946668587
WordScorePoS
include0VB
vulnerability0NN
identify0VB
impact0.0194583992186006NN
integrate0.0537696560159885VB
resource0.0449504361447405NN
plan0.0583751976558017NN
alternative0NN
systemic0JJ
term0NN

Label: 14, with a score of: 0.342757153378646
WordScorePoS
include0VB
vulnerability0NN
identify0.0531983120572613VB
impact0.042459973210532NN
integrate0VB
resource0.0572167664438909NN
plan0.0141533244035107NN
alternative0NN
systemic0JJ
term0NN

For example technological advances such as recycled wastewater and desalination of seawater may be useful to address scarcity at the local level

Label: 6, with a score of: 0.923047661245008
WordScorePoS
technological0JJ
advance0NN
recycle0.103230094596636VB
wastewater0.204998018174509NN
desalination0.0683326727248364NN
scarcity0.273330690899345NN
local0.0181797964452808JJ
level0.00647010760354177NN

Flood management often under the responsibility of local governments can be addressed in part by rainwater harvesting and the use of ecosystem services

Label: 15, with a score of: 0.576077919309354
WordScorePoS
flood0.0316735178673487NN
management0.0486060126274891NN
responsibility0NN
local0.0275274877486226JJ
government0NN
address0NN
rainwater0NN
harvest0NN
ecosystem0.134075577362426NN
service0.0127200410376242NN

Label: 6, with a score of: 0.413089326887809
WordScorePoS
flood0.041835863322702NN
management0.0428007293971684NN
responsibility0NN
local0.0181797964452808JJ
government0NN
address0NN
rainwater0NN
harvest0.041835863322702NN
ecosystem0.0295155485671997NN
service0.0084006124696649NN

Label: 14, with a score of: 0.359147984475113
WordScorePoS
flood0NN
management0.0499817979003487NN
responsibility0NN
local0JJ
government0NN
address0NN
rainwater0.0531983120572613NN
harvest0.0325700316331562NN
ecosystem0.0229784274580034NN
service0NN

Label: 12, with a score of: 0.327967687479493
WordScorePoS
flood0NN
management0.0339811641917276NN
responsibility0NN
local0.0144336476662975JJ
government0NN
address0NN
rainwater0NN
harvest0.0664302389559493NN
ecosystem0.0234335422829798NN
service0.00666957305782837NN

An essential part of this integrated approach is the involvement of all stakeholders as BUILDING SUSTAINABILITY IN AN URBANIZING WORLD stakeholders have the capacity to redefine some of the objectives and constraints when such flexibility is necessary to avoid an impasse

Label: 17, with a score of: 0.732538368792701
WordScorePoS
essential0JJ
integrate0VB
approach0NN
stakeholder0.0739231123597559NN
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sustainability0.036961556179878NN
world0.00418824502414673NN
capacity0.0451108483130998NN
objective0.0299586785922821NN
constraint0NN
avoid0VB

The IRP approach can also be used to manage water demand

Label: 6, with a score of: 0.881183025865558
WordScorePoS
approach0NN
manage0VB
water0.556409482163189NN
demand0NN

Label: 12, with a score of: 0.381905516495379
WordScorePoS
approach0.0542519672965169NN
manage0VB
water0.186896403054502NN
demand0NN

Warming processes will change demand patterns as result of shifts in precipitation more evaporation and more extensive droughts

Label: 13, with a score of: 0.894409357135054
WordScorePoS
warm0.207053776511125JJ
process0NN
change0.402565546215053NN
demand0NN
pattern0.0414107553022249NN
result0NN
drought0NN

Conservation incentives and disincentives to outdoor water use may become essential if warming processes increase water demands especially during peak demand periods when both water supply and electric power capacities are stretched to their limits

Label: 6, with a score of: 0.869982423058482
WordScorePoS
conservation0NN
incentive0NN
water0.556409482163189NN
essential0.0251182378961834JJ
warm0JJ
process0NN
increase0.00905621749519576NN
demand0NN
period0NN
supply0.0590310971343993NN
power0NN
capacity0.0104991907358033NN
limit0NN

Label: 12, with a score of: 0.338531368873455
WordScorePoS
conservation0NN
incentive0NN
water0.186896403054502NN
essential0JJ
warm0JJ
process0NN
increase0.00958677785775002NN
demand0NN
period0.0409791940864438NN
supply0.0703006268489394NN
power0NN
capacity0.00833571598658786NN
limit0NN

Label: 13, with a score of: 0.137603137842053
WordScorePoS
conservation0NN
incentive0NN
water0NN
essential0JJ
warm0.207053776511125JJ
process0NN
increase0.0145316075945746NN
demand0NN
period0NN
supply0NN
power0NN
capacity0.025270503924109NN
limit0.0335649154577099NN

Can Adaptation Plans Reach the Slums

Label: 11, with a score of: 0.860252730673623
WordScorePoS
adaptation0.04477822029736NN
plan0.0583751976558017NN
reach0NN
slum0.146277152173395NN

Label: 13, with a score of: 0.332129753416713
WordScorePoS
adaptation0.0671298309154199NN
plan0.0291713033871158NN
reach0NN
slum0NN

Although planning for climate change is crucial there is plenty of evidence that most urbanization in developing countries takes place outside of any plan or official regulations

Label: 13, with a score of: 0.820810705193999
WordScorePoS
plan0.0291713033871158NN
climate0.291881734282273NN
change0.402565546215053NN
crucial0JJ
evidence0NN
urbanization0NN
develop0VB
country0NN
regulation0NN

Label: 11, with a score of: 0.34621539531338
WordScorePoS
plan0.0583751976558017NN
climate0.0229054731708393NN
change0.0315914058171786NN
crucial0JJ
evidence0NN
urbanization0.146277152173395NN
develop0VB
country0NN
regulation0NN

Label: 10, with a score of: 0.322731410931392
WordScorePoS
plan0.0344256159926965NN
climate0NN
change0NN
crucial0JJ
evidence0.12939607756667NN
urbanization0NN
develop0VB
country0NN
regulation0.0977392570420122NN

The reasons include unaffordable housing in well regulated areas ruralto urban and transnational migration and the mismatch in institutional capacity to manage the growth of urban centers and economies

Label: 11, with a score of: 0.800621278849596
WordScorePoS
include0VB
housing0.219415728260093NN
regulate0VB
urban0.27622591898666JJ
transnational0JJ
migration0NN
institutional0JJ
capacity0.0112376090361851NN
manage0VB
growth0NN
economy0NN

Label: 10, with a score of: 0.414059045648983
WordScorePoS
include0VB
housing0NN
regulate0VB
urban0.0488696285210061JJ
transnational0JJ
migration0.12939607756667NN
institutional0JJ
capacity0NN
manage0.0660824365173543VB
growth0.11178243319132NN
economy0NN

Label: 8, with a score of: 0.309909409937102
WordScorePoS
include0VB
housing0NN
regulate0VB
urban0JJ
transnational0JJ
migration0NN
institutional0JJ
capacity0.0123650647908625NN
manage0VB
growth0.208565600657229NN
economy0.0821985033584295NN

In Africa Asia and Latin America hundreds of millions of people live in sub par conditions dealing with problems such as overcrowding insecure tenure and inadequate provision of water sanitation health electricity and other services

Label: 6, with a score of: 0.858420503843326
WordScorePoS
africa0IN
asia0IN
hundred0CD
million0CD
people0.024149913320522NNS
live0.0252018374089947VB
condition0NN
inadequate0.0836717266454039JJ
provision0NN
water0.556409482163189NN
sanitation0.334686906581616NN
health0NN
electricity0.0348974218717993NN
service0.0084006124696649NN

Label: 3, with a score of: 0.261381500301384
WordScorePoS
africa0IN
asia0IN
hundred0CD
million0CD
people0.00539943740774902NNS
live0.0300513417930436VB
condition0NN
inadequate0JJ
provision0.0249430985430622NN
water0.0382775468083574NN
sanitation0.0249430985430622NN
health0.191387734041787NN
electricity0NN
service0.0100171139310145NN

Label: 12, with a score of: 0.225586782371986
WordScorePoS
africa0IN
asia0IN
hundred0CD
million0CD
people0.0119834723221875NNS
live0VB
condition0.0409791940864438NN
inadequate0JJ
provision0NN
water0.186896403054502NN
sanitation0NN
health0.0339811641917276NN
electricity0NN
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This is particularly true in informal settlements which comprise up to percent of the urban populations in some developing country cities

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Two factors limit city capacity to address these issues

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First urban governments tend to have limited human and fiscal capacity finance is usually controlled nationally and even when cities produce most of the country GDP they are often dependent on higher levels of government for their resources

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Second urban governments tend to have an antagonistic relationship with low income groups particularly informal settlers who are believed to hold back city success by remaining outside the formal economic system

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WordScorePoS
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Label: 8, with a score of: 0.159443336119263
WordScorePoS
urban0JJ
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Photo Julianne Baker Gallegos World Bank BOX The Cost of Adapting Housing and Slums Planned adaptation costs do not account for the high adaptation costs for urban housing which are largely private

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The World Bank estimated annual average household investments in urban housing in response to climate change at

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WordScorePoS
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billion per year in rising to

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WordScorePoS
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Photo Julianne Baker Gallegos World Bank These costs would be even higher if they also accounted for slums

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Most informal settlements in developing countries share characteristics that intensify the vulnerability of their residents to climate change

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WordScorePoS
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WordScorePoS
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WordScorePoS
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These include poorly constructed buildings inadequate infrastructure lack of safe drinking water drainage and sanitation services and severe overcrowding with attendant public health impacts

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In regions prone to flooding floods are more severe in sprawling urban spaces than in inland towns in part because of weak urban planning

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The growth of urban slums increases the risk of climate related disasters such as flooding and landslides in part because natural ecosystem based storm breaks and rain catchment areas are increasingly converted to public buildings and housing developments

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WordScorePoS
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Source World Bank

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Cities will have to rethink their relationship with informality if they are to protect informal housing and informal economies from climate impacts

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Strategic land use planning in urban areas can help prevent residential and industrial development in high risk areas but it can simultaneously increase the cost of legal housing and service provision

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This limits the possibilities for low and middleincome households to rent or purchase adequate accommodations Sattherthwaite et al

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On top of that the need to adapt urban homes to climate change imposes further costs that are not usually counted in government plans Box

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Assessing Risks and Developing Resilience As cities develop tools for managing climatic stresses and adapting to changing environment they become more resilient

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The Stockholm Resilience Center defines resilience as the capacity of system to continually change and adapt yet remain within critical thresholds

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Such resilience needs to be designed into policies for sustainable http www

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stockholmresilience

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org BUILDING SUSTAINABILITY IN AN URBANIZING WORLD management of social ecologic and economic systems Levin et al

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Derissen et al

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Resilient systems have the capacity to absorb external shocks and continue to function by reorganizing and adapting

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In fact change and disruptions can create opportunities for development innovation and prosperity in resilient system Levin et al

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Holling

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By managing for resilience and understanding that cities are exposed to uncertainty rather than seeing them as static systems we increase the likelihood that development can be sustained under changing climatic and environmental conditions Folke et al

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As Holling and Walker put it resilient socioecological system is synonymous with region [in this case city] that is ecologically economically and socially sustainable

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Essential Considerations for Resilience in Urban Systems ' governance networks and the support provided by government to its society ' the ability of society to learn adapt and reorganize to meet urban challenges ' social dynamics between citizens as community members users of services and consumers and ' society relationship to the built environment which determines urban form and spatial relations

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http isecoeco

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org pdf resilience

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pdf FIG

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According to the Resilience Alliance the resilience of urban systems is in part determined by metabolic flows that sustain urban functions and societal well being see Chapter and Figure

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Among the other essential considerations in building urban resilience are http www

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resalliance

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org Metabolic Flows Governance Networks Production supply and consumption chains Institutional structures and organisations Urban Resilience Social Dynamics Built Environment Demographics human capital and inequity Ecosystem services in urban landscapes Source Adapted from Resilience Alliance

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resalliance

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php urban resilience Photo Shutterstock Adaptive capacity is also contingent on individual household community or institutional resources for example income asset base knowledge social networks and effective and climate resilient service provision

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Particularly in urban areas the quality and reach of public infrastructure and service provision is key especially for vulnerable populations Sattherthwaite et al

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Hence resilient cities are in essence urban areas that support sustainable income generation good quality of service and infrastructure provision and access to health education and information systems

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In accordance with this the Swedish Environmental Advisory Council argues that policies that manage for resilience will ' stimulate flexible and open institutions that encourage learning ' provide incentives for inclusion and cohesion among different stakeholders and across sectors and disciplines ' encourage ecosystem friendly technology and economic incentives that enhance resilience and adaptive capacity Ibid ' develop indicators for gradual change as well as early warning systems to prevent shifts toward less desirable states ' acknowledge uncertainty and expect the unexpected and ' strengthen the perception of humanity nature and economic systems as interdependent

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Other key measures to build resilience include ' urban planning for example directing future growth away from risk or rezoning existing areas ' investing in infrastructure for example building sea walls drainage systems and earthquakeresistant construction ' leveraging ecosystem services for example managing coastal ecosystems to mitigate erosion and storm surges ' fostering social resilience for example strengthening community awareness and coping strategies and ' creating insurance mechanisms to manage both public and private financial risks

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Ultimately assessments of urban risk are the foundation for resilience and adaptation action plans

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These risk assessments and action plans should be developed with the involvement of all relevant stakeholders

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Urbanization itself is sometimes seen as driver of climate related risk but this can obscure the underlying more specific risk factors

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Urban planners can benefit from deconstructing the local causes and mechanisms of urbanization and identifying the challenges and opportunities to build resilience within the system Sattherthwaite et al

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They can ask what is leading residents and industries to concentrate in high risk areas and develop urban plans that address these factors to promote urban development in areas exposed to lower climate related risk

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Preliminary data exists on the risks cities are facing from climate change Figure as well as on the actions they are taking in response

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While there is still long way to go in data driven and evidencebased policy making it is important to push these efforts forward because urban development initiatives that consider the impacts of climate change will be more durable in the long term

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The next chapter looks at how sustainability and resilience could be better measured and monitored across the world cities

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Further Reading Annex shows multi hazard risk assessment of the largest urban areas

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Annex reviews existing rankings of the world most at risk cities

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Annex details the urban data that Earth observation satellites can provide and its uses in planning and disaster risk management

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PART II THE PATH TO SUSTAINABILITY Measuring Urban Sustainability Key Messages ' Urban metabolism analyses look at how cities consume produce and transform materials and energy

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As measures of urban sustainability these are more comprehensive and credible than traditional ecological footprint

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' Data sources for cities have been improving and metabolism indicators are now being calculated regularly and rigorously

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All cities should begin measuring material flows and other environmental and social data

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The Large Urban Areas Compendium initiated by the World Bank aims to support this

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' More standardized data enables cities to be compared in typology

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While requiring strong assumptions this type of analysis sheds light on how cities are evolving in terms of their economic growth urbanization and greenhouse gas emissions

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Increasing wealth and urbanization usually lead to greater emissions but some cities and countries have reversed the trend

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Poor cities face greater challenge in doing so given their limited capacities

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' In addition to metrics for efficiency and environmental impacts cities need credible standardized Urban Resilience Index

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This would help focus attention on the urgency of mitigating risks from climate change in cities

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To assess city sustainability we need to consider how urban systems contribute positively to growth prosperity and social well being but also their level of congestion costs such as pollution greenhouse gas emissions and overcrowding

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The benefits of density and agglomeration economies need to offset the costs of congestion for the city to continue to grow

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Quantifying these trade offs is not easy but it is of great importance to policy makers

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In this chapter we use the framework of urban metabolism to understand how cities consume produce and transform resources

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We then consider how to select indicators to evaluate the sustainability of these urban processes

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As part of the Sustainable Cities Partnership the World Bank has begun program to track key indicators in the world largest cities

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Using two indicators greenhouse gas emissions per capita and GDP per capita we develop simple typology for comparing and benchmarking cities

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Finally we consider how to apply similar metrics to assess cities resilience to climate change impacts

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Urban Metabolism The concept of urban metabolism means of analyzing city resource needs and pollution problems originated with Abel Wolman

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Wolman first applied the idea to hypothetical U

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city of million inhabitants analyzing the flux of water food and fuel into the city and then out again in the form of sewage solid refuse and air pollutants

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More generally in the field of industrial metabolism the flow of materials and energy through chain of extraction production use and disposal is assessed in order to measure the impacts of anthropogenic activity on the environment Fischer Kowalski

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Today the problems associated with these fluxes are even more widely recognized as threats to sustainable development

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The framework of urban metabolism can be used to measure not only environmental impacts but also the economic and social dimensions of sustainable cities

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Data on the consumption of material resources and energy can indicate the efficiency and intensity of economic production and the potential limits to growth

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When metabolism information is spatially disaggregated data on access to resources and penetration of urban services can be used as measures of social inclusion

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Research in this field has grown in the last decade with urban metabolism studies currently supported by the European Union Schremmer and Stead the State of California Energy Commission and the World Bank

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Figure shows one example discussed further in Annex

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Table lists over cities or regions for which urban metabolism studies have been conducted in some form

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Some of these have focused on particular substances while others have been more comprehensive

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The list would be much longer if it also included cities that have completed green TABLE Examples of Urban Metabolism Studies house gas inventories which entail collecting data on the energy inputs and waste outputs of cities for examples of these cities see Kennedy et al

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Urban metabolism studies to date have typically included fluxes of energy nutrients and materials as well as the urban hydrologic cycle Schremmer and Stead

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In the broader context of economic environmental and social sustainability urban metabolism might be defined as the sum total of the technical and socioeconomic processes that occur in cities resulting in growth production of energy and goods and elimination of waste Kennedy Cuddihy and Engel Yan

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Conversely sustainable development in the specific context of urban metabolism can be defined as development without increases in the throughput of materials and energy beyond the biosphere capacity for regeneration and waste assimilation Goodland and Daly

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Hence any city aiming to develop sustainably must be aware of its metabolism the inputs outputs and changes in storage of energy materials nutrients water and wastes

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Such data is necessary for determining city North America Europe Asia Australia Los Angeles Brussels Amman Sydney Miami Gävle Sweden Bangkok Brisbane and southeast Moncton New Brunswick Geneva Beijing Queensland Phoenix Hamburg Hong Kong Toronto Leipzig Jakarta Limerick Ireland Shenzhen Lisbon Singapore London Taipei Paris Tokyo Africa Cape Town Dar es Salaam Prague Swiss lowlands Stockholm Vienna York Source Kennedy

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See this reference for further sources and details

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PART II THE PATH TO SUSTAINABILITY FIG

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The Urban Metabolism of Amman Jordan Total Radiation ktCO Landfill Waste Greenhouse Gas Emissions

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MJ kt ktCO Organic Waste kt Paper Cardboard Textiles kt Plastics Glass Metal kt Carbon Dioxide ktCO Methane ktCO Nitrous Oxide Other Materials ktCO kt ktCO ktCO ktCO ktCO Commercial Institutional Residential Manufacturing Industry Wastewater Road Transport ktCO Cropland Aviation Marine D Losses Consumption ktCO GWh MCM Water Supply GWh MCM Electricity Generation GWh Renewables Fuel Oil Diesel Oil Natural Gas GWh GWh GWh GWh Natural Gas Fuel Oil LPG Kerosene Diesel Oil Gasoline Jet Kerosene Marine Fuel Oil TJ TJ TJ TJ TJ TJ TJ TJ Fossil Fuels Source Reprinted from Sugar Kennedy and Hoornweg

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD greenhouse gas emissions and it can also be used in the analysis of specific issues such as waste management or the supply of water and other scarce resources

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Measuring Inputs and Outputs For cities that are serious about sustainability quantification of urban metabolism is becoming mainstream activity and there is growing need for comparable standardized approach to measure inputs and outputs

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Two recent developments may help to meet this need comprehensive scientific framework for urban metabolism and draft list of ideal urban metabolism parameters

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A generic urban metabolism framework was developed at workshop at MIT in January attended by urban industrial ecology researchers

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The framework comprehensively captures all biophysical stocks and flows in the urban metabolism by integrating the Eurostat Material Flow Analysis model with methods of energy substance and water flow analysis Figure

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The system boundary will usually correspond to the political boundaries of city or to the amalgamation of city boundaries within metropolitan region

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It includes peri urban activities such as food production and forestry where applicable

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Furthermore natural components of urban metabolism such as solar radiation and groundwater fluxes are included together with anthropogenic stocks and flows

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Table shows draft list of the categories of urban metabolism parameters that cities should ideally measure

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The list was vetted by participants at Sustainable Urban Systems workshop in June at the International Society of Industrial Ecology Sixth International Conference in Berkeley California

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Its contents reflect the FIG

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Standard Urban Metabolism Classification System Source Reprinted from Kennedy and Hoornweg

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Note Urban systems boundary broadly showing inflows outflows internal flows storage and production of biomass minerals water and energy

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PART II THE PATH TO SUSTAINABILITY Inflows Biomass food wood Production Biomass Minerals Fossil Fuel transport heating industrial Minerals metals construction materials Outflows Stocks Waste Emissions gases solid wastewater other liquids Infrastructure Buildings construction materials metals wood other materials Heat Other [machinery durable] metals other materials Substances Produced goods Substances Electricity kWh Natural energy Water Drinking [surface groundwater] Precipitation Substances e

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energy0.494617163780726NN
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drinking0NN

Label: 6, with a score of: 0.515176139677708
WordScorePoS
sustainability0NN
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energy0.0153390539205676NN
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drinking0.206460189193271NN

Label: 12, with a score of: 0.441813885320264
WordScorePoS
sustainability0.0409791940864438NN
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Label: 9, with a score of: 0.338665145004977
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Label: 2, with a score of: 0.180863966033554
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Label: 13, with a score of: 0.128234792704942
WordScorePoS
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Label: 17, with a score of: 0.0905675363874853
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nutrients Produced goods Source Discussion among participants in the Sustainable Urban Systems workshop at International Society of Industrial Ecology Sixth International Conference Berkeley California June

Label: 9, with a score of: 0.573031349585899
WordScorePoS
nutrient0NN
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international0.00265959362690358JJ
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industrial0.318320101004075JJ
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Label: 11, with a score of: 0.491101221540452
WordScorePoS
nutrient0NN
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Label: 5, with a score of: 0.354449608451382
WordScorePoS
nutrient0NN
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sustainable0.00390173629172995JJ
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Note Units are indicated in parentheses

Label: 0, with a score of: 1

t tons joules kWh kilowatt hours

Label: 15, with a score of: 1
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urgency of pressing urban environmental issues as well as knowledge of data availability and quality

Label: 11, with a score of: 0.744119394170253
WordScorePoS
urban0.27622591898666JJ
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quality0.0136980135278128NN

Label: 17, with a score of: 0.45890649105153
WordScorePoS
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Included are metabolism parameters required for accounting of both direct and indirect greenhouse gas emissions

Label: 13, with a score of: 0.70010643584924
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Label: 7, with a score of: 0.565249925918161
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Label: 12, with a score of: 0.312798336373047
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The list also includes metrics that address other issues such local air pollution waste management sustainable water use and management of nutrients

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water0.556409482163189NN
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Label: 12, with a score of: 0.49911396521468
WordScorePoS
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Label: 11, with a score of: 0.314422593473214
WordScorePoS
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Since cities together have such large global impacts all large cities should begin collecting urban metabolism data

Label: 11, with a score of: 0.988798438470086
WordScorePoS
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The methodology described above is robust standardized and practical enough that cities should be able to use it with relative ease

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It is well anchored in academic literature and complements related efforts that cities are already undertaking

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However collecting data at the city level can be challenging for local governments

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While all the parameters in Table provide important parts of the picture tracking the flows of many different types of goods and materials may become an overwhelming task and some are more difficult to measure than others

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To establish standard set of urban metabolism measures and begin regular accounting of material flows cities will need to become more proficient at data collection and dissemination

Label: 11, with a score of: 0.914615458378931
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Label: 17, with a score of: 0.310259530725884
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Annex shows data requirements for abbreviated urban metabolism studies which can be undertaken by cities with limited resources or institutional capacity Kennedy and Hoornweg

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The spread of technologies such as Earth observation satellites may help to fill in additional data Box

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Label: 9, with a score of: 0.3952764839441
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Label: 14, with a score of: 0.379767908243251
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Label: 7, with a score of: 0.343292428062871
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It is also useful to look at urban indicators that are being measured in some existing initiatives

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TABLE Categories of Urban Metabolism Parameters BOX Earth Observation Satellite datasets are increasingly used to drive assessment and analysis of spatial environmental and temporal patterns of urban growth such as urban expansion land use and housing densities and they are becoming standard reference technology in urban indicator monitoring and evaluation

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WordScorePoS
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Label: 8, with a score of: 0.137248175542599
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The major benefit of Earth observation EO based monitoring is detailed and cost effective digital mapping which ensures that decision makers and planners have the most up to date and accurate data available on land use and land cover

Label: 15, with a score of: 0.524212416996497
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Label: 14, with a score of: 0.346394546144335
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Historical EO data archives also enable tracking of changes over time providing insight into the evolution of urban agglomerations

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WordScorePoS
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Label: 17, with a score of: 0.313307588800657
WordScorePoS
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Earth observation facilitates the collection of measurements in harmonized and standardized manner allowing spatially and temporally consistent global comparisons

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Samples from Spatial Comparisons of Delhi Mumbai and Dhaka Source Reprinted from Eoworld project GISAT for European Space Agency World Bank http go

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worldbank

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org EGEFL

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The potential uses for EO are many and varied given that several key factors affect the extent and patterns of urban expansion economic development population growth demand for housing extension of transport networks and so on

Label: 11, with a score of: 0.706722972697363
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Label: 8, with a score of: 0.376626913839778
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Label: 10, with a score of: 0.359388850422972
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a Within an integrated geo information environment the spatial information can be combined with ancillary statistical economic and social data allowing for more elaborate analysis

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Label: 9, with a score of: 0.420730597176167
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Label: 12, with a score of: 0.386624131606743
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Label: 1, with a score of: 0.34666613549587
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Examples are discussed in further detail in Annex

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eea

Label: 0, with a score of: 1

europa

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eu data and maps data urban atlas mapping guide urban atlas mapping guide final

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pdf at download file PART II THE PATH TO SUSTAINABILITY it easier for cities to compare and share information about their operations and the well being of their residents

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With around participants the GCIF compiles Web based datasets provided by its members through standardized methodology using an established set of metrics

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Of these are core indicators required from all members are supporting indicators that all cities are encouraged to collect and are profile indicators basic statistics to help cities identify other peer cities for comparative learning

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At present while the GCIF is developing and testing this initial set of indicators only cities that contribute their data gain access to the collective datasets

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The GCIF represents unique resource for measuring cities progress toward sustainability and other performance goals and the indicators are now undergoing standardization by the International Organization for Standardization ISO

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Tracking Progress with City Indicators Setting goals for improved urban performance or well being has little purpose if there is no way to measure progress toward such targets

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While there is as yet no consensus on how to define and measure urban sustainability it is clear that in rigorous metrics are needed

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Label: 10, with a score of: 0.338845366991473
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Along with urban metabolism measurements could also focus important aspects of sustainability such as resilience greenhouse gas emissions and energy intensity provision of basic services and social equity among others

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Label: 12, with a score of: 0.266385271129384
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The need to monitor and manage city goals has led to proliferation of urban indicator systems of varying scope size and focus

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Two of the most ambitious are ICLEI STAR Community Index and the Global City Indicators Facility GCIF which is based at the University of Toronto

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In addition there are number of other projects with similar aims ' STAR is strategic planning and performance management system [offering] local governments road map for improving community sustainability

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It is currently in development with pilot cities and counties

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It will eventually be linked to set of consulting services that ICLEI provides its member communities to help them deal with climate change financing and other sustainability and operational challenges

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' The GCIF Annex was established in with funding support from the World Bank based on standardized set of indicators that the Bank developed to build globally comparable information on cities

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The GCIF is now supported by the Government of Ontario and number of international agencies and corporate partners

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The indicators are designed to make http www

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cityindicators

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icleiusa

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org sustainability star community index ' The OECD Metropolitan Regions database provides range of socioeconomic indicators for OECD metropolitan regions including population density labor force characteristics GDP and productivity rates and employment and participation rates

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To contribute to the understanding of the effects of urban dynamics on the environment and the well being of urban residents the OECD is currently expanding its metropolitan database to include small set of environmental indicators to monitor the environmental performance of cities

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Given the requirement of comparability the OECD has prioritized indicators that can be derived from global sources notably data from remote sensing and geographic information systems GIS tools

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' The collaboration between and CDP Cities CDP is another successful partnership for collecting and disseminating http www

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oecd

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org gov regional policy regionalstatisticsandindicators

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Label: 10, with a score of: 0.446179385629691
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htm BUILDING SUSTAINABILITY IN AN URBANIZING WORLD standardized greenhouse gas emissions data and related climate change information from cities

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C and CDP have collaborated to bring annual reporting standard practice in the private sector to city governments

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CDP reporting system is used by over global organizations to make their climate changerelated data available to the marketplace including major cities

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In addition to these comprehensive indicator programs other groups are promoting more specialized or geographically localized urban metric systems

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Label: 2, with a score of: 0.340126357240077
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' The Partnership for Urban Risk Reduction PURR is collaboration among United Cities and Local Governments the Earthquake and Megacities Initiative Metropolis CityNet and ICLEI designed to help cities prepare more effectively for natural hazards and disasters

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While the primary goal is to provide information they also propose an Urban Risk Index to quantify cities vulnerability

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' With more of an emphasis on the built environment Siemens Green City Indices Annex have now been released for several continents

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They are intended to facilitate learning by ranking cities environmental performance

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' Other measurement schemes are focused on specific aspects of urban systems such as energy efficiency social cohesion or public health or on more restricted geographic ranges such as one region country or metropolitan area or different parts of single city

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These initiatives span range of diverse metrics and methods

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Measuring city performance consistently is surprisingly difficult and cities are long http www

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emi megacities

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org purr way from agreement on common approach

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A survey of eight city governments in North and South America Hoornweg et al

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found that each regularly tracked dozens to hundreds of indicators but only two of the total metrics were common to all eight cities

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The way in which information was stored and analyzed also varied widely

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This is particularly problem for investment strategies and international policies intended to help cities around the world achieve global sustainability goals

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Such programs cannot be successful without having consistent urban data upon which to evaluate decisions

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As with corporate led urban initiatives datacentric programs face scaling challenge

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They need to have sufficiently large user base so that other cities feel compelled to join leading to adoption of standard methods

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Thus one of the central questions is organizational who has the authority to prioritize among non standardized approaches and how can the growing interest and enthusiasm for urban sustainability be more effectively stimulated and channeled

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The Large Urban Areas Compendium new initiative from the World Bank backed by the Sustainable Cities Partnership is starting this process

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The Large Urban Areas Compendium Urban indicators need to be measured standardized targeted and compared across cities and over time

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The Large Urban Areas Compendium Annex is first step toward identifying what data should be collected on regular basis in order to focus policy making on underperforming sectors

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Currently most statistical information is collected at the national level whereas many relevant policy decisions are made The cities were Belo Horizonte Bogotá Cali Montreal Porto Alegre São Paulo Toronto and Vancouver

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PART II THE PATH TO SUSTAINABILITY FIG

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The World Largest Urban Areas Source developed by Katie McWilliams and authors with data obtained from http www

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citymayors

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com and implemented at the local level

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Standard urban indicators should be designed to bridge the gap between the scale at which information is available and the level at which urban development is conducted

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To begin addressing this gap the World Bank and partners such as the World Economic Forum and the World Business Council for Sustainable Development WBCSD will assemble on an annual basis existing key data and indicators for the largest urban areas in the world Figure and Annex

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With continuous updating of this compendium better definition and data quality for all key metrics should emerge

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These data can also be used to develop typologies for comparing and analyzing cities at different levels of sustainability and development see next section

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The Large Urban Areas Compendium will complement and extend the work of the GCIF

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WordScorePoS
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Label: 10, with a score of: 0.361350500860205
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GCIF is now finalizing an ISO standard methodology for data collection and GCIF member cities have already started submitting information on many urban indicators but many have not yet released their data publicly

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GCIF selected their indicators based on input from the partner cities to ensure that they address city priorities information needs and challenges

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The indicators were also designed to be meaningful to cities across the globe regardless of geography culture affluence size or political structure

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WordScorePoS
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To minimize duplication of efforts and additional burdens on local governments the Large Urban Areas Compendium draws heavily on the existing GCIF metrics

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WordScorePoS
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The set of indicators included in the compendium has several goals but primarily these data are intended to present vital signs

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They should provide snapshot of basic city functions and amenities while diagnosing any problems and suggesting possible directions for improvement

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In addition some of the indicators were chosen http cityindicators

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org themes

Label: 0, with a score of: 1

aspx BUILDING SUSTAINABILITY IN AN URBANIZING WORLD to monitor progress toward the Millennium Development Goals

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Label: 16, with a score of: 0.344568181443374
WordScorePoS
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WordScorePoS
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Additional indicators deemed necessary for the compendium include GCIF core indicators were adapted for the following themes ' certain geographic and demographic characteristics ' economic data such as GDP and the Gini coefficient of income distribution which provides measure of economic inequality in the area ' Economy ' Energy ' energy consumption energy intensity of the economy and electricity use ' Emissions and pollution ' Water sanitation and waste management ' Shelter ' greenhouse gas emissions and intensity of the economy ' urban metabolism indicators such as water consumption and solid waste generation ' Governance ' Transportation ' Education technology and innovation ' Health ' measures of disaster risk institutional capacity and vulnerability including vulnerability to the impacts of climate change ' infrastructure inventories and need and ' other health indicators

Label: 7, with a score of: 0.530310139110375
WordScorePoS
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Label: 12, with a score of: 0.443021397295059
WordScorePoS
additional0JJ
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WordScorePoS
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Label: 6, with a score of: 0.32942950553026
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Label: 11, with a score of: 0.277615684983041
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Label: 9, with a score of: 0.218827896677187
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Label: 10, with a score of: 0.216368634675221
WordScorePoS
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Label: 8, with a score of: 0.184005796606881
WordScorePoS
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Label: 3, with a score of: 0.119870098511435
WordScorePoS
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Label: 4, with a score of: 0.0493804118120943
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BOX Where Are the Borders of the Largest Cities

Label: 11, with a score of: 0.975520407963838
WordScorePoS
border0NN
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There is currently no consensus on the borders of the largest urban areas almost all of which are metropolitan areas made up of several municipalities

Label: 11, with a score of: 0.87469908224939
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Label: 10, with a score of: 0.359624233412754
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Sydney for example is made up of local governments

Label: 17, with a score of: 0.772868484631331
WordScorePoS
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a Internationally the Lord Mayor of the City of Sydney may represent Sydney but only percent of the metropolitan population is electorally represented by the mayor

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WordScorePoS
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Urban areas might also be defined by regional or national governments economic hinterlands commuter sheds or other service hinterlands such as employment or travel nodes

Label: 11, with a score of: 0.591060704605592
WordScorePoS
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Label: 8, with a score of: 0.511232011386177
WordScorePoS
urban0JJ
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Label: 1, with a score of: 0.325832826018552
WordScorePoS
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For most of the truly significant municipal accomplishments in urban transportation energy conservation and solid waste disposal metropolitan or regional approaches are necessary

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WordScorePoS
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Label: 11, with a score of: 0.591136971333345
WordScorePoS
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Label: 12, with a score of: 0.415321808437222
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gnb

Label: 0, with a score of: 1

nsw

Label: 0, with a score of: 1

gov

Label: 0, with a score of: 1

au The initial dataset for the Large Urban Areas Compendium is published in Annex of this report

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This is the first version of what is hoped to be an annual process

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Label: 5, with a score of: 0.571091222812535
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The samples in Annex represent the best data currently available but they have significant gaps and considerable ranges of estimates particularly for the greenhouse gas emissions

Label: 13, with a score of: 0.733151102667027
WordScorePoS
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Label: 7, with a score of: 0.42804189185435
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Label: 9, with a score of: 0.331833095075663
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Given the fundamental importance of cities and urban areas to the world economy and environment such paucity of data is unacceptable

Label: 11, with a score of: 0.953915113941087
WordScorePoS
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It is illustrative that there is no consensus even on what the world largest urban areas are or where their borders lie see Box

Label: 11, with a score of: 0.838099199197061
WordScorePoS
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Label: 10, with a score of: 0.345155210107689
WordScorePoS
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Today there are better statistical data for example on Fiji population than there are for Delhi Lagos Rio de Janeiro or Shanghai all of which have populations in excess of million

Label: 17, with a score of: 0.468636378422529
WordScorePoS
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For future annual compendia organizations such as the GCIF Metropolis and national governments as well as PART II THE PATH TO SUSTAINABILITY the individual urban areas and their constituent local governments would be asked to move toward consensus definition of at least the largest urban areas of the world

Label: 11, with a score of: 0.826117892014482
WordScorePoS
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Label: 10, with a score of: 0.355080769687109
WordScorePoS
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For this inaugural effort the list of largest urban areas is taken from the City Mayors Foundation

Label: 11, with a score of: 0.982651945579453
WordScorePoS
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As cities improve their data reporting through programs like GCIF temporary best available data approach could be used to monitor the world largest urban areas

Label: 11, with a score of: 0.931014091280514
WordScorePoS
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This data would not be new primary data but would instead be compilation of what is being collected and published by cities agencies and higher levels of government

Label: 11, with a score of: 0.883570947110766
WordScorePoS
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Label: 17, with a score of: 0.302868765706367
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Eventually hierarchy of data credibility is likely to emerge for example city reported data consistent with ISO standards through agencies like GCIF would be the gold standard while estimated values such as those for greenhouse gas emissions in Annex are intended as placeholder values

Label: 11, with a score of: 0.715088206231056
WordScorePoS
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emission0.0373517915079663NN

Label: 13, with a score of: 0.366105311265481
WordScorePoS
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emission0.19598742448524NN

Label: 12, with a score of: 0.353609275927237
WordScorePoS
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The list of urban areas is expected to change as broader consensus emerges on borders and of course as populations change

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Refinement is expected during the next several years as GCIF develops an aggregation function for its member cities and as national governments and city based agencies reach broader consensus on the definition of major urban areas

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For the foreseeable future this list is expected to be published in several venues such as the City Mayors Web site

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Ideally the methodology used to develop the list will be sufficiently robust to enable ISO standardization

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The hope is that this broad set of indicators will be made available by cities updated annually and shared through related publications and Web sites

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In particular changes in the indicators over time will be extremely relevant for public policy decision making

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The next section offers basic examples of the type of analysis that can be conducted using indicator data

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citymayors

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com Typology of the Largest Cities Indicator data can be used to create typology of cities comparing them along various dimensions of sustainability and clustering cities with similar patterns

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A typology can reveal relationships for example environmental burdens increasing along with wealth

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It also identifies outliers that defy these tradeoffs and do far better than most cities and can suggest the reasons for such differences

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In sum typology can identify core sustainability challenges and help find ways to secure people well being while simultaneously taking advantage of opportunities to decouple urban development from carbon and resource intensity

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Underlying this methodology is the assumption that experiences of cities at more affluent stages of economic development are useful to developing country cities as they follow or avoid development paths used in the past

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Success stories and cautionary tales may allow developing cities to take preventative measures or institute policies that will lead to lower carbon development

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With this in mind typology based on richer dataset could serve as baseline for planners and the public to measure progress toward sustainability

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Furthermore in the absence of binding international agreements around climate change typology could motivate smaller scale partnerships among cities within the same cluster or type

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Awareness of other cities within the same peer group can facilitate tailored collaboration and action on certain touchstone issues and enable peer to peer learning

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Sophisticated typologies are challenging to build however

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If defining sustainability based on limited number of indicators is fraught with difficulty categorizing cities according to their level of sustainability is even more complicated

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Cities are not easily clustered by income production density or even pollution as variables combine to produce complex effects and categories are unclear

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD important component

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For example sustainability could be equally measured by water consumption or waste disposal

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However greenhouse gas emissions are more closely related to global warming

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Similarly in this preliminary work we have used GDP per capita as proxy for well being but there are undoubtedly many other dimensions along which it must be measured

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These complications however should not hinder the exercise of clustering cities in pragmatic ways

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Here this section proposes relatively simple typology of cities

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It situates cities along two dimensions economic development measured by GDP per capita and one indicator of sustainability namely greenhouse gas emissions per capita

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Together these two measures show the greenhouse gas intensity of GDP growth calculated as emissions GDP

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In Figure lists the largest urban areas based on www

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citymayors

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com to analyze why cities at similar levels of development and income can exhibit different levels of sustainability

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This graph is based in part on estimated data and thus it only roughly Of course reduction of greenhouse gas intensity is not the only measure of urban sustainability sustainability is wide and controversial concept and low carbon development is only one albeit World Bank High Income Classification FIG

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Typology of the Largest Urban Areas Based on Emissions and GDP SYDNEY Melbourne Atlanta Detroit WASHINGTON DC Tehran Guangzhou DALLAS HOUSTON Phoenix BOSTON LOS ANGELES CHICAGO TORONTO PHILADELPHIA NEW YORK SAN FRANCISCO LONDON GHG Emissions per Capita tCO yr Johannesburg SHANGHAI TIANJIN BANGKOK Ankara Algiers Moscow BEIJING Hanoi QUADRANT Chengdu Shenyang Bandung ATHENS QUADRANT II Caracas Alexandria Istanbul Riyadh Montreal Berlin St

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Petersburg Xian MIAMI Milan Jeddah Hong Kong CAPE TOWN Osaka SINGAPORE Pusan MADRID Monterrey Guadalajara Medellin PARIS TOKYO Pune Salvador Lahore CHONGQING B

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Horizonte Fortaleza Jakarta Bogota Hydearabad Bangalore Ho Chi Minh Khartoum Recife Lima Chennai Mumbai QUADRANT IV Santiago Cairo SEOUL BUENOS AIRES P

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Alegre BARCELONA Brasilia MEXICO CITY QUADRANT III RIO DE JANEIRO Karachi Lagos Kinshasa ppm by ppm by DELHI SAO PAULO AHMADABAD CALCUTTA GDP per Capita PEER REVIEWED METROPOLITAN LEVEL DATA Estimate Source Author calculations see data in Annex

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Note Capitalized city names indicate that data from peer reviewed metropolitan level greenhouse gas inventories was used

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Greenhouse gas emissions for all other cities were estimated based on sectoral activity and national emissions factors

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Values are provided and estimated for the world largest urban areas as listed on the City Mayors website

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Estimates are indicative and not directly comparable to values from actual greenhouse gas inventories

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PART II THE PATH TO SUSTAINABILITY FIG

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Label: 17, with a score of: 0.669768072253467
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Per Capita Carbon Dioxide Emissions from Four Major Chinese Cities Beijing Chongqing Shanghai Tianjin Source Adapted from Dhakal

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indicates the relative positions of cities

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Better clustering requires high quality open data from cityscale greenhouse gas emissions inventories

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Nonetheless the four city types approximated by the quadrants in Figure already point to useful information

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In particular cities are grouped according to their level of income as classified by the World Bank and whether their per capita emissions are within certain limits

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Specifically are city per capita emissions below the level of global per capita emissions according to the two IPCC scenariosof or parts per million p

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global0.00501208088147356JJ

Label: 13, with a score of: 0.488562322447044
WordScorePoS
city0NN
capita0NN
emission0.19598742448524NN
level0.0415277415054218NN
global0.0375696197697663JJ

of carbon dioxide equivalent

Label: 13, with a score of: 0.58798369112273
WordScorePoS
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
equivalent0JJ

Label: 14, with a score of: 0.406558027922048
WordScorePoS
carbon0.0271683202880942NN
dioxide0.0401833161726777NN
equivalent0JJ

Label: 12, with a score of: 0.400996150406107
WordScorePoS
carbon0NN
dioxide0NN
equivalent0.0664302389559493JJ

Label: 7, with a score of: 0.377368665435273
WordScorePoS
carbon0.0625160381065121NN
dioxide0NN
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The four categories are ' Quadrant Per capita emissions above the p

Label: 13, with a score of: 0.804503859659699
WordScorePoS
capita0NN
emission0.19598742448524NN

Label: 12, with a score of: 0.363806693573092
WordScorePoS
capita0.0332151194779746NN
emission0.0554128417527413NN

Label: 11, with a score of: 0.337133424069552
WordScorePoS
capita0.04477822029736NN
emission0.0373517915079663NN

scenario and far above the p

Label: 13, with a score of: 1
WordScorePoS
scenario0.0548233071065585NN

scenario and low or medium income annual GDP per capita of or less

Label: 10, with a score of: 0.582825052680229
WordScorePoS
scenario0NN
medium0NN
income0.166475342664294NN
gdp0NN
capita0NN

Label: 9, with a score of: 0.546584096475008
WordScorePoS
scenario0NN
medium0.0454743001434393NN
income0.110649350233433NN
gdp0NN
capita0NN

Label: 8, with a score of: 0.385311882798794
WordScorePoS
scenario0NN
medium0.0607878664258118NN
income0NN
gdp0NN
capita0.049270765107907NN

' Quadrant II Emissions above the p

Label: 13, with a score of: 0.906096780895015
WordScorePoS
emission0.19598742448524NN

scenario and high income

Label: 10, with a score of: 0.734256629399062
WordScorePoS
scenario0NN
income0.166475342664294NN

Label: 9, with a score of: 0.488030345199119
WordScorePoS
scenario0NN
income0.110649350233433NN

' Quadrant III Emissions below the p

Label: 13, with a score of: 0.906096780895015
WordScorePoS
emission0.19598742448524NN

scenario and high income

Label: 10, with a score of: 0.734256629399062
WordScorePoS
scenario0NN
income0.166475342664294NN

Label: 9, with a score of: 0.488030345199119
WordScorePoS
scenario0NN
income0.110649350233433NN

' Quadrant IV Emissions below the p

Label: 13, with a score of: 0.906096780895015
WordScorePoS
emission0.19598742448524NN

scenario and even below the p

Label: 13, with a score of: 1
WordScorePoS
scenario0.0548233071065585NN

scenario in many cases with low to medium income

Label: 10, with a score of: 0.642042934092903
WordScorePoS
scenario0NN
medium0NN
income0.166475342664294NN

Label: 9, with a score of: 0.602119719143015
WordScorePoS
scenario0NN
medium0.0454743001434393NN
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Quadrant lower income and high emissions contains the three largest Chinese cities Shanghai Tianjin and Beijing with the fourth largest city Chongqing only slightly below the p

Label: 11, with a score of: 0.937346199874609
WordScorePoS
income0NN
emission0.0373517915079663NN
largest0JJS
city0.607697021770652NN
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slightly0RB

Label: 13, with a score of: 0.146644325149344
WordScorePoS
income0NN
emission0.19598742448524NN
largest0JJS
city0NN
beij0NN
slightly0RB

Label: 10, with a score of: 0.124562401608832
WordScorePoS
income0.166475342664294NN
emission0NN
largest0JJS
city0NN
beij0NN
slightly0RB

line in Quadrant IV

Label: 8, with a score of: 0.597739203601743
WordScorePoS
line0.0410992516792148NN

Label: 11, with a score of: 0.543236900840252
WordScorePoS
line0.0373517915079663NN

Label: 9, with a score of: 0.447157854853483
WordScorePoS
line0.030745604615229NN

Label: 15, with a score of: 0.384254299762362
WordScorePoS
line0.0264204925485796NN

Dhakal discusses the energy use and increasing carbon emissions of these four cities Figure and the underlying BUILDING SUSTAINABILITY IN AN URBANIZING WORLD drivers and policy implications

Label: 11, with a score of: 0.698029924650526
WordScorePoS
energy0.0492535935240667NN
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carbon0.0373517915079663NN
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city0.607697021770652NN
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world0.00626002771693937NN
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Label: 7, with a score of: 0.537265128736136
WordScorePoS
energy0.494617163780726NN
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carbon0.0625160381065121NN
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figure0NN
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sustainability0NN
world0.00261936653839306NN
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Label: 13, with a score of: 0.246218593298092
WordScorePoS
energy0.0123065238088614NN
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carbon0.0559964069957828NN
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city0NN
figure0NN
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building0NN
sustainability0NN
world0.00351929966767313NN
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Label: 17, with a score of: 0.227656876628436
WordScorePoS
energy0.0109843026967392NN
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carbon0NN
emission0NN
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underlie0VB
building0.149940347231585NN
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world0.00418824502414673NN
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Label: 12, with a score of: 0.222906637541122
WordScorePoS
energy0.133960988253756NN
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carbon0NN
emission0.0554128417527413NN
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figure0NN
underlie0VB
building0NN
sustainability0.0409791940864438NN
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policy0.0203215381458133NN

Economic growth particularly in the industrial sector until was found to be the dominant driver of carbon emissions

Label: 9, with a score of: 0.566788423468305
WordScorePoS
economic0.0320338181740375JJ
growth0.0260040237236563NN
industrial0.318320101004075JJ
sector0.0565629593551219NN
dominant0JJ
carbon0NN
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Label: 8, with a score of: 0.474242419664265
WordScorePoS
economic0.128463821595706JJ
growth0.208565600657229NN
industrial0JJ
sector0.0252035516550537NN
dominant0JJ
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Label: 13, with a score of: 0.384117871024706
WordScorePoS
economic0JJ
growth0NN
industrial0.0414107553022249JJ
sector0NN
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carbon0.0559964069957828NN
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Label: 7, with a score of: 0.323959502434683
WordScorePoS
economic0JJ
growth0NN
industrial0JJ
sector0NN
dominant0.122412709674631JJ
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During the period Shanghai economic growth was significantly higher than that of the other cities resulting in the rapid increase of carbon dioxide emissions

Label: 11, with a score of: 0.778215473647429
WordScorePoS
period0NN
economic0.0389167984372011JJ
growth0NN
city0.607697021770652NN
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rapid0.0373517915079663JJ
increase0.00323105002784081NN
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Label: 8, with a score of: 0.351509221405026
WordScorePoS
period0NN
economic0.128463821595706JJ
growth0.208565600657229NN
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increase0.00711043475673939NN
carbon0NN
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Label: 13, with a score of: 0.314520084295025
WordScorePoS
period0NN
economic0JJ
growth0NN
city0NN
result0NN
rapid0JJ
increase0.0145316075945746NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN

During the energy intensity measured by greenhouse gas per output GDP declined as the economic structure shifted from manufacturing to tertiary sectors

Label: 7, with a score of: 0.755352983090037
WordScorePoS
energy0.494617163780726NN
intensity0.122412709674631NN
measure0NN
greenhouse0.0749457204978913NN
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gdp0NN
decline0NN
economic0JJ
structure0NN
manufacture0NN
tertiary0JJ
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Label: 9, with a score of: 0.377681066383524
WordScorePoS
energy0.0540565419125769NN
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measure0NN
greenhouse0NN
gas0NN
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economic0.0320338181740375JJ
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manufacture0.240811982686599NN
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sector0.0565629593551219NN

Label: 12, with a score of: 0.279830650964108
WordScorePoS
energy0.133960988253756NN
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greenhouse0.0332151194779746NN
gas0.0332151194779746NN
gdp0NN
decline0.0234335422829798NN
economic0.0433009429988926JJ
structure0NN
manufacture0NN
tertiary0JJ
sector0.0169905820958638NN

Label: 8, with a score of: 0.185586031940578
WordScorePoS
energy0NN
intensity0NN
measure0.0347609334428716NN
greenhouse0NN
gas0NN
gdp0NN
decline0NN
economic0.128463821595706JJ
structure0NN
manufacture0NN
tertiary0JJ
sector0.0252035516550537NN

This decline in intensity slowed down in the becoming negligible in the cases of Shanghai and Tianjin and absolute levels of emissions have continued to rise

Label: 13, with a score of: 0.802461636902369
WordScorePoS
decline0.0236803262479443NN
intensity0NN
slow0JJ
level0.0415277415054218NN
emission0.19598742448524NN
continue0.0403047273067529VB
rise0.120914181920259NN

Label: 7, with a score of: 0.351309579265944
WordScorePoS
decline0NN
intensity0.122412709674631NN
slow0JJ
level0NN
emission0.0625160381065121NN
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Chongqing substantially lower emissions are an artifact of its designation in as one of China four directly controlled municipalities along with the other three largest cities Shanghai Tianjin and Beijing

Label: 11, with a score of: 0.911223600754486
WordScorePoS
substantially0RB
emission0.0373517915079663NN
control0NN
largest0JJS
city0.607697021770652NN
beij0NN

Label: 13, with a score of: 0.276860235947601
WordScorePoS
substantially0RB
emission0.19598742448524NN
control0NN
largest0JJS
city0NN
beij0NN

Though all four cities are overseen by single Mayor in the case of Chongqing the municipality jurisdiction was extended over districts and counties giving it land area larger than Taiwan

Label: 11, with a score of: 0.937739650920546
WordScorePoS
city0.607697021770652NN
single0JJ
extend0VB
give0VB
land0.0389167984372011NN

As such the core city had population of million in but according to article from the official Xinhua News Agency the municipality has total population of

Label: 11, with a score of: 0.908780687610658
WordScorePoS
city0.607697021770652NN
population0.0194583992186006NN
agency0NN
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million including

Label: 0, with a score of: 1

million farmers

Label: 2, with a score of: 1
WordScorePoS
farmer0.175025014152344NN

Hence the lowerconsumption lifestyle of the rural residents decreases the value of per capita emissions for the municipality as whole while obscuring the impacts of the higherintensity urbanized area

Label: 13, with a score of: 0.500344839990464
WordScorePoS
lifestyle0NN
rural0JJ
decrease0NN
capita0NN
emission0.19598742448524NN
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Label: 2, with a score of: 0.310910076476544
WordScorePoS
lifestyle0NN
rural0.148975141598503JJ
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Label: 12, with a score of: 0.622273335740249
WordScorePoS
lifestyle0.122937582259331NN
rural0JJ
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capita0.0332151194779746NN
emission0.0554128417527413NN
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Label: 11, with a score of: 0.44260692469797
WordScorePoS
lifestyle0NN
rural0.0373517915079663JJ
decrease0.0731385760866977NN
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emission0.0373517915079663NN
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Quadrant II high income and high emissions is dominated by the cities of the United States for which carbon dioxide emissions from road transport cars and trucks and residential buildings electricity and other fuels account for approximately percent of national carbon dioxide emissions Brown

Label: 11, with a score of: 0.648737225282616
WordScorePoS
income0NN
emission0.0373517915079663NN
city0.607697021770652NN
unite0VB
carbon0.0373517915079663NN
dioxide0NN
road0.04477822029736NN
transport0.0747035830159326NN
car0NN
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electricity0NN
fuel0NN
account0.0194583992186006NN
approximately0RB
national0.00501208088147356JJ

Label: 13, with a score of: 0.561495318485214
WordScorePoS
income0NN
emission0.19598742448524NN
city0NN
unite0.0828215106044499VB
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
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electricity0NN
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approximately0RB
national0.0112708859309299JJ

Label: 17, with a score of: 0.155790132211543
WordScorePoS
income0.017987180284335NN
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city0NN
unite0.036961556179878VB
carbon0NN
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road0NN
transport0.0249900578719309NN
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fuel0NN
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approximately0RB
national0.0134132459226336JJ

Label: 10, with a score of: 0.123301199416877
WordScorePoS
income0.166475342664294NN
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city0NN
unite0VB
carbon0NN
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road0NN
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car0NN
residential0JJ
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electricity0NN
fuel0NN
account0.0172128079963482NN
approximately0RB
national0.00886732612541978JJ

Label: 7, with a score of: 0.316994066547128
WordScorePoS
income0.0449973847138318NN
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city0NN
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carbon0.0625160381065121NN
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residential0JJ
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electricity0.0625160381065121NN
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This is consistent with the trend of sprawling U

Label: 14, with a score of: 0.735995756275504
WordScorePoS
consistent0.0401833161726777JJ

Label: 17, with a score of: 0.676986149595727
WordScorePoS
consistent0.036961556179878JJ

cities and the growth of periurban and suburban communities with large singlefamily detached houses

Label: 11, with a score of: 0.911187883997837
WordScorePoS
city0.607697021770652NN
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Label: 8, with a score of: 0.312725653622376
WordScorePoS
city0NN
growth0.208565600657229NN
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To some extent this is also http news

Label: 13, with a score of: 1
WordScorePoS
extent0.0548233071065585NN

xinhuanet

Label: 0, with a score of: 1

com english china

Label: 0, with a score of: 1

htm true of greater London which has relatively low population density and large numbers of residents who live in the suburbs and commute to work

Label: 11, with a score of: 0.586473474411309
WordScorePoS
population0.0194583992186006NN
density0.0731385760866977NN
live0.0269743071558856VB

Label: 10, with a score of: 0.500150530428289
WordScorePoS
population0.0860640399817412NN
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live0.0159075630937118VB

These developed cities are already above the level of per capita emissions that would lead to greenhouse gas concentrations of p

Label: 11, with a score of: 0.786899166551756
WordScorePoS
develop0VB
city0.607697021770652NN
level0.00692515656684925NN
capita0.04477822029736NN
emission0.0373517915079663NN
lead0NN
greenhouse0NN
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Label: 13, with a score of: 0.481792122904717
WordScorePoS
develop0VB
city0NN
level0.0415277415054218NN
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emission0.19598742448524NN
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greenhouse0.0671298309154199NN
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triggering global warming of more than °C and irreversible ecological damage according to predictions from the IPCC Metz et al

Label: 13, with a score of: 0.952185341920362
WordScorePoS
global0.0375696197697663JJ
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and Stern

Label: 0, with a score of: 1

In contrast some other European cities in Quadrant II Madrid and Paris have denser urban form smaller multi unit housing and more extensive public transport networks which reduces car dependency

Label: 11, with a score of: 0.952844293963505
WordScorePoS
city0.607697021770652NN
paris0NN
urban0.27622591898666JJ
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multus0NN
housing0.219415728260093NN
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reduce0.0200483235258943VB
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Label: 16, with a score of: 0.158302709339574
WordScorePoS
city0NN
paris0NN
urban0JJ
form0.158049144643351NN
multus0NN
housing0NN
public0.0285977592242194NN
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reduce0.0220985725470694VB
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Accordingly they fall lower in the quadrant but still above the threshold for p

Label: 3, with a score of: 0.845468641911302
WordScorePoS
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Label: 10, with a score of: 0.335659199285875
WordScorePoS
fall0NN
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Label: 2, with a score of: 0.302681797934743
WordScorePoS
fall0.0440683498519077NN
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Very few cities can be found in the high income low greenhouse gas emissions quadrant

Label: 11, with a score of: 0.793855234347435
WordScorePoS
city0.607697021770652NN
income0NN
greenhouse0NN
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emission0.0373517915079663NN

Label: 13, with a score of: 0.406431843146062
WordScorePoS
city0NN
income0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN

Label: 10, with a score of: 0.204879567938604
WordScorePoS
city0NN
income0.166475342664294NN
greenhouse0NN
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emission0NN

Label: 7, with a score of: 0.316785635878162
WordScorePoS
city0NN
income0.0449973847138318NN
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Quadrant III includes Buenos Aires Mexico City and Porto Alegre

Label: 11, with a score of: 0.997380969185672
WordScorePoS
include0VB
city0.607697021770652NN

Mexico has the fourth largest installed geothermal capacity in the world accounting for percent of the total generation mix Geothermal Energy Association

Label: 7, with a score of: 0.875671046393119
WordScorePoS
largest0JJS
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Label: 12, with a score of: 0.315664922469262
WordScorePoS
largest0JJS
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Mexico City Climate Action Program emphasizes efficient urban transportation with the installation of bus rapid transit lines renewal of the taxi and microbus fleets construction of bicycle and pedestrian routes and restriction of days when automobiles can be operated

Label: 11, with a score of: 0.884830241956968
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.283475048770455
WordScorePoS
city0NN
climate0.291881734282273NN
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Label: 1, with a score of: 0.279680689789204
WordScorePoS
city0NN
climate0.0376648791150643NN
action0.0269969049497485NN
urban0JJ
transportation0NN
rapid0JJ
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day0.25973846344589NN
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Barcelona is an extremely dense metropolitan area with highly developed public transport system

Label: 11, with a score of: 0.6984783391013
WordScorePoS
highly0RB
develop0VB
public0.0583751976558017NN
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system0.0315914058171786NN

Quadrant IV low emissions and low income contains predominantly middle income Southeast Asian South Asian African and Latin American cities

Label: 11, with a score of: 0.765596725979093
WordScorePoS
emission0.0373517915079663NN
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south0RB
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Label: 10, with a score of: 0.395173123882579
WordScorePoS
emission0NN
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Label: 13, with a score of: 0.232613916079191
WordScorePoS
emission0.19598742448524NN
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african0IN
city0NN

Label: 9, with a score of: 0.334109252640576
WordScorePoS
emission0NN
income0.110649350233433NN
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south0RB
african0IN
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Geothermal power is significant contributor in some of these countries with the Philippines generating percent of its electricity from geothermal sources it is the world second largest producer behind the United States although geothermal represents only

Label: 7, with a score of: 0.516084093981712
WordScorePoS
power0NN
contributor0.122412709674631NN
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generate0VB
electricity0.0625160381065121NN
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Label: 2, with a score of: 0.515899362617023
WordScorePoS
power0NN
contributor0NN
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percent of U

Label: 0, with a score of: 1

power generation Holm p

Label: 9, with a score of: 0.834979942798687
WordScorePoS
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Label: 12, with a score of: 0.501628465987825
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Geothermal PART II THE PATH TO SUSTAINABILITY energy represents percent of national power generation in Indonesia which is the third largest producer in the world

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WordScorePoS
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Label: 12, with a score of: 0.463213248373038
WordScorePoS
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Label: 9, with a score of: 0.349759798994041
WordScorePoS
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Hydropower is dominant in Brazil percent of national generation and represents percent of the power mix in India

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WordScorePoS
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Label: 9, with a score of: 0.4273834814328
WordScorePoS
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Label: 6, with a score of: 0.364740037560209
WordScorePoS
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Both are among the top largest hydroelectric producers in the world REN

Label: 2, with a score of: 0.871914466330912
WordScorePoS
largest0.0714379839896614JJS
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Many Quadrant IV cities are growing rapidly and as they continue to develop they are likely to move into Quadrant II unless policies prevent this

Label: 11, with a score of: 0.921553772110177
WordScorePoS
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For example car use is still much lower in Quadrant IV cities than in the U

Label: 11, with a score of: 0.993450567531736
WordScorePoS
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cities of Quadrant II

Label: 11, with a score of: 0.997380969185672
WordScorePoS
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However sprawling urban growth coupled with economic growth will encourage private car ownership

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WordScorePoS
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Label: 11, with a score of: 0.348519080090243
WordScorePoS
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Label: 10, with a score of: 0.446407743282608
WordScorePoS
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Label: 1, with a score of: 0.302484723829543
WordScorePoS
urban0JJ
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Hence integrated public transport and land use planning is essential especially when addressing service delivery and housing provision for informal and low income settlements

Label: 11, with a score of: 0.773277277370136
WordScorePoS
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Label: 10, with a score of: 0.332210239200029
WordScorePoS
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Energy use in housing and buildings is another factor in these cities emissions

Label: 11, with a score of: 0.831392699392361
WordScorePoS
energy0.0492535935240667NN
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Label: 7, with a score of: 0.468622317949973
WordScorePoS
energy0.494617163780726NN
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Label: 13, with a score of: 0.175202613188165
WordScorePoS
energy0.0123065238088614NN
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Label: 12, with a score of: 0.159288304621038
WordScorePoS
energy0.133960988253756NN
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Label: 17, with a score of: 0.135358801466588
WordScorePoS
energy0.0109843026967392NN
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Air conditioning is not yet widespread even amongst the middle and higher income residents and because the Quadrant IV cities have mostly tropical climates heating is not necessary

Label: 11, with a score of: 0.795019949638043
WordScorePoS
air0.0373517915079663NN
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Label: 13, with a score of: 0.347406710903585
WordScorePoS
air0NN
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Label: 10, with a score of: 0.198144126365985
WordScorePoS
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Label: 7, with a score of: 0.33614672680542
WordScorePoS
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However as with private automobiles penetration of air conditioning and other appli Photo Shutterstock ances into the market will increase as household incomes grow

Label: 10, with a score of: 0.532980973503726
WordScorePoS
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Label: 9, with a score of: 0.445624510033682
WordScorePoS
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Label: 2, with a score of: 0.438776517057325
WordScorePoS
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Label: 12, with a score of: 0.347105149812913
WordScorePoS
private0JJ
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In the building sector this growth could be curtailed by the adoption of low energy designs that employ natural ventilation techniques

Label: 7, with a score of: 0.687112120511231
WordScorePoS
building0NN
sector0NN
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Label: 8, with a score of: 0.324747359609653
WordScorePoS
building0NN
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Label: 17, with a score of: 0.287419999781169
WordScorePoS
building0.149940347231585NN
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Label: 12, with a score of: 0.28050777133315
WordScorePoS
building0NN
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Label: 9, with a score of: 0.336599380242565
WordScorePoS
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Vernacular pre electricity architecture may suggest culturally and environmentally appropriate building designs particularly for residential structures

Label: 17, with a score of: 0.711772916024635
WordScorePoS
electricity0NN
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Label: 16, with a score of: 0.392944382470433
WordScorePoS
electricity0NN
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In rapidly urbanizing cities this may be an important way to lower the impact of the immense number of new housing units needed in the coming decades

Label: 11, with a score of: 0.972518539002673
WordScorePoS
rapidly0RB
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An analysis of particular cities in each quadrant could shed more light on what is behind the different emissions levels in the four quadrants

Label: 11, with a score of: 0.925154377370532
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.3370352278945
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Greater pollution in some cities could be due to larger manufacturing sector urban sprawl or the use of coal based energy for example

Label: 11, with a score of: 0.773191728856672
WordScorePoS
pollution0.0315914058171786NN
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Label: 7, with a score of: 0.49450481798185
WordScorePoS
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Label: 9, with a score of: 0.281646917729802
WordScorePoS
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Label: 12, with a score of: 0.174519693269881
WordScorePoS
pollution0.0234335422829798NN
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This kind of analysis could reveal the weight of each factor in explaining variations of greenhouse gas emissions

Label: 13, with a score of: 0.793416237536867
WordScorePoS
kind0NN
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Label: 7, with a score of: 0.510307433061606
WordScorePoS
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Decoupling GDP from Emissions Eventually sustainability should lead to the improvement of the urban well being and inclusiveness of growth

Label: 8, with a score of: 0.577699651708314
WordScorePoS
decouple0.0804764811724088VB
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Label: 11, with a score of: 0.533669668588711
WordScorePoS
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Label: 13, with a score of: 0.333545849632018
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decouple0VB
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Label: 7, with a score of: 0.329001998770218
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decouple0VB
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It was therefore important that our city typology differentiate between more and less greenhouse gas intensive cities that are BUILDING SUSTAINABILITY IN AN URBANIZING WORLD World Bank High Income Classification FIG

Label: 11, with a score of: 0.928012873982857
WordScorePoS
city0.607697021770652NN
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Label: 17, with a score of: 0.178267193206125
WordScorePoS
city0NN
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Label: 10, with a score of: 0.122545716275477
WordScorePoS
city0NN
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Greenhouse Gas Intensity Versus GDP Per Capita Bandung Tehran GHG Emissions per GDP ktCO bn Salvador BEIJING SHANGHAI Hanoi Kinshasa Shenyang Xian BANGKOK Lagos Algiers Lahore Source Author calculations see data in Annex

Label: 13, with a score of: 0.602738193739708
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greenhouse0.0671298309154199NN
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Label: 7, with a score of: 0.637480937988405
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greenhouse0.0749457204978913NN
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Note See Figure for description of the data shown

Label: 9, with a score of: 0.819417641095815
WordScorePoS
figure0.0602029956716497NN
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Label: 17, with a score of: 0.573196937763079
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Caracas Ankara Moscow Hyderabad Alexandria CHONGQING Chennai Istanbul Bangalore Jakarta Riyadh Karachi Ho Chi Minh Khartoum Mumbai Jeddah Medellin Lima Bogota Santiago B

Label: 0, with a score of: 1

Horizonte DELHI BUENOS AIRES AHMADABAD RIO DE JANEIRO DHAKA MEXICO CITY SAO PAULO Melbourne Johannesburg Berlin ATHENS CAPE TOWN Monterrey SEOUL Brasilia SYDNEY Guangzhou Detroit Milan TORONTO MIAMI Montreal Hong Kong MADRID Atlanta LOS ANGELES DALLAS Phoenix CHICAGO PHILADELPHIA SINGAPORE LONDON TOKYO PARIS BARCELONA HOUSTON NEW YORK WASHINGTON DC BOSTON SAN FRANCISCO GDP per Capita at similarly high levels of economic development

Label: 11, with a score of: 0.892341274870296
WordScorePoS
city0.607697021770652NN
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Label: 8, with a score of: 0.240450040860715
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city0NN
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Quadrant III in Figure shows the lower intensity cities where well being has been decoupled from emissions

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figure0NN
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Label: 13, with a score of: 0.276117745016186
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However certain Quadrant II cities with high greenhouse gas emissions and high GDP per capita also have economies with low greenhouse gas intensity Figure

Label: 11, with a score of: 0.642407637440237
WordScorePoS
city0.607697021770652NN
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Label: 13, with a score of: 0.510796829910341
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Label: 7, with a score of: 0.509610693918248
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This can be explained by the fact that GDP growth in low intensity developed cities far outstrips population growth

Label: 11, with a score of: 0.724116048122877
WordScorePoS
gdp0NN
growth0NN
intensity0NN
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Label: 8, with a score of: 0.531062887292117
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gdp0NN
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Label: 10, with a score of: 0.357498719707354
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For example in Chicago New York and Los Angeles the ratios of GDP growth rate to population growth rate are

Label: 8, with a score of: 0.681607346534335
WordScorePoS
ratio0NN
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Label: 10, with a score of: 0.509857219390607
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ratio0NN
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Label: 1, with a score of: 0.343627776751254
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London GDP growth rate is times the rate of its population growth

Label: 8, with a score of: 0.655800465237739
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gdp0NN
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Label: 10, with a score of: 0.577222129449182
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Label: 1, with a score of: 0.33061741046057
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On the other hand in the developing cities with high greenhouse gas intensity the population growth is similar to or even greater than GDP growth in some cases

Label: 11, with a score of: 0.686885237760568
WordScorePoS
develop0VB
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Label: 8, with a score of: 0.503758007503183
WordScorePoS
develop0VB
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Label: 10, with a score of: 0.339117733575786
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develop0VB
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In Beijing Shanghai and Lagos the ratio of GDP growth to population growth is

Label: 8, with a score of: 0.755913021496395
WordScorePoS
beij0NN
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growth0.208565600657229NN
population0.0428212738652355NN

Label: 10, with a score of: 0.508862403797445
WordScorePoS
beij0NN
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growth0.11178243319132NN
population0.0860640399817412NN

Label: 5, with a score of: 0.302878757848614
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Kinshasa GDP is growing only percent as fast as its population

Label: 10, with a score of: 0.722270775226772
WordScorePoS
gdp0NN
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Hence the discrepancy between high greenhouse gas intensity and low GDP per capita stems at least in part from the fact that as developing city economies grow their de carbonization occurs more slowly than the rate at which their populations grow

Label: 11, with a score of: 0.768746870346591
WordScorePoS
greenhouse0NN
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Label: 7, with a score of: 0.39349414311731
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greenhouse0.0749457204978913NN
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Decoupling urban growth from emissions may be most important in middle income countries

Label: 10, with a score of: 0.518357122124748
WordScorePoS
decouple0VB
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growth0.11178243319132NN
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income0.166475342664294NN
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Label: 11, with a score of: 0.49688664844271
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decouple0VB
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emission0.0373517915079663NN
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Label: 8, with a score of: 0.458008163503361
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decouple0.0804764811724088VB
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Label: 13, with a score of: 0.310556303047753
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decouple0VB
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emission0.19598742448524NN
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income0NN
country0NN

Label: 9, with a score of: 0.311933209714214
WordScorePoS
decouple0VB
urban0JJ
growth0.0260040237236563NN
emission0NN
middle0.0602029956716497JJ
income0.110649350233433NN
country0NN

As noted above Quadrant contains several fastgrowing middle income cities

Label: 11, with a score of: 0.91379948453014
WordScorePoS
middle0JJ
income0NN
city0.607697021770652NN

Label: 10, with a score of: 0.250330472034176
WordScorePoS
middle0JJ
income0.166475342664294NN
city0NN

Figure shows that while high income countries account for percent of the world GDP they actually produce only percent of the world greenhouse gas emissions

Label: 13, with a score of: 0.569853220254257
WordScorePoS
figure0NN
income0NN
country0NN
account0NN
world0.00351929966767313NN
gdp0NN
produce0VB
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN

Label: 10, with a score of: 0.319701970143288
WordScorePoS
figure0NN
income0.166475342664294NN
country0NN
account0.0172128079963482NN
world0.00276879546803864NN
gdp0NN
produce0VB
greenhouse0NN
gas0NN
emission0NN

Label: 7, with a score of: 0.443743407549364
WordScorePoS
figure0NN
income0.0449973847138318NN
country0NN
account0NN
world0.00261936653839306NN
gdp0NN
produce0VB
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN

Label: 12, with a score of: 0.375209760968427
WordScorePoS
figure0NN
income0NN
country0NN
account0.0577345906651901NN
world0.00464349782489754NN
gdp0NN
produce0.0332151194779746VB
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
emission0.0554128417527413NN

Label: 9, with a score of: 0.324426508322972
WordScorePoS
figure0.0602029956716497NN
income0.110649350233433NN
country0NN
account0.0160169090870187NN
world0.00257642711761688NN
gdp0NN
produce0VB
greenhouse0NN
gas0NN
emission0NN

This relationship is reversed for the upper middle income countries which account PART II THE PATH TO SUSTAINABILITY Low Income Lower Middle Income

Label: 10, with a score of: 0.676411235618516
WordScorePoS
reverse0NN
middle0JJ
income0.166475342664294NN
country0NN
account0.0172128079963482NN
sustainability0NN

Label: 9, with a score of: 0.613216335499574
WordScorePoS
reverse0NN
middle0.0602029956716497JJ
income0.110649350233433NN
country0NN
account0.0160169090870187NN
sustainability0NN

Low Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Lower Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Share of Total World Population

Label: 2, with a score of: 0.437246439750072
WordScorePoS
share0.0365427838531249NN
total0.0252000451111224NN
world0.0124838525528648NN
population0.0310434135749871NN

Label: 7, with a score of: 0.389734615270964
WordScorePoS
share0.0383371018038178NN
total0.0528748274225028NN
world0.00261936653839306NN
population0NN

Label: 10, with a score of: 0.368973173239162
WordScorePoS
share0NN
total0NN
world0.00276879546803864NN
population0.0860640399817412NN

Label: 17, with a score of: 0.335659313257665
WordScorePoS
share0.0766240489430275NN
total0NN
world0.00418824502414673NN
population0NN

billion in Percent b

Label: 0, with a score of: 1

Share of Total World GDP US

Label: 7, with a score of: 0.504041780586866
WordScorePoS
share0.0383371018038178NN
total0.0528748274225028NN
world0.00261936653839306NN
gdp0NN

Label: 17, with a score of: 0.434106469622492
WordScorePoS
share0.0766240489430275NN
total0NN
world0.00418824502414673NN
gdp0NN

Label: 2, with a score of: 0.398729959061368
WordScorePoS
share0.0365427838531249NN
total0.0252000451111224NN
world0.0124838525528648NN
gdp0NN

Label: 9, with a score of: 0.356090906473317
WordScorePoS
share0.0377086395700813NN
total0.0260040237236563NN
world0.00257642711761688NN
gdp0NN

Label: 14, with a score of: 0.310229462682066
WordScorePoS
share0NN
total0NN
world0.0045533140757086NN
gdp0.0531983120572613NN

trillion in Percent Low Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Low Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Lower Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Lower Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Share of Total World GHG Emissions

Label: 13, with a score of: 0.639218106046805
WordScorePoS
share0NN
total0NN
world0.00351929966767313NN
emission0.19598742448524NN

Label: 7, with a score of: 0.500935730697643
WordScorePoS
share0.0383371018038178NN
total0.0528748274225028NN
world0.00261936653839306NN
emission0.0625160381065121NN

Label: 12, with a score of: 0.342581880201316
WordScorePoS
share0NN
total0.0468670845659596NN
world0.00464349782489754NN
emission0.0554128417527413NN

billion tCO in Percent d

Label: 0, with a score of: 1

Share of Total World Population in Cities with Million or More Residents

Label: 11, with a score of: 0.924189261631587
WordScorePoS
share0NN
total0NN
world0.00626002771693937NN
population0.0194583992186006NN
city0.607697021770652NN

billion in Percent Low Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Low Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Lower Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Lower Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

High Income

Label: 10, with a score of: 0.756711939782219
WordScorePoS
income0.166475342664294NN

Label: 9, with a score of: 0.502955471427553
WordScorePoS
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Upper Middle Income

Label: 10, with a score of: 0.651236533539157
WordScorePoS
middle0JJ
income0.166475342664294NN

Label: 9, with a score of: 0.668358975651015
WordScorePoS
middle0.0602029956716497JJ
income0.110649350233433NN

Share of Total Municipal Solid Waste Generation Mtonnes in Percent f

Label: 12, with a score of: 0.694786494277853
WordScorePoS
share0NN
total0.0468670845659596NN
municipal0JJ
waste0.138532104381853NN
generation0.0819583881728876NN

Label: 9, with a score of: 0.401920747042255
WordScorePoS
share0.0377086395700813NN
total0.0260040237236563NN
municipal0JJ
waste0NN
generation0.0909486002868787NN

Label: 7, with a score of: 0.399495374868018
WordScorePoS
share0.0383371018038178NN
total0.0528748274225028NN
municipal0JJ
waste0.0625160381065121NN
generation0NN

Share of Total Municipal Solid Waste Generation Mtonnes projected in Percent Source Hoornweg and Bhada Tata

Label: 12, with a score of: 0.699986946897133
WordScorePoS
share0NN
total0.0468670845659596NN
municipal0JJ
waste0.138532104381853NN
generation0.0819583881728876NN
project0.0664302389559493NN
source0NN

Label: 7, with a score of: 0.380008848852002
WordScorePoS
share0.0383371018038178NN
total0.0528748274225028NN
municipal0JJ
waste0.0625160381065121NN
generation0NN
project0NN
source0.0274787313211514NN

Label: 9, with a score of: 0.352680890255491
WordScorePoS
share0.0377086395700813NN
total0.0260040237236563NN
municipal0JJ
waste0NN
generation0.0909486002868787NN
project0NN
source0.0135141354781442NN

FIG

Label: 0, with a score of: 1

GDP and Greenhouse Gas Emissions by Country Income BUILDING SUSTAINABILITY IN AN URBANIZING WORLD FIG

Label: 13, with a score of: 0.579873707151171
WordScorePoS
gdp0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
country0NN
income0NN
building0NN
sustainability0NN
world0.00351929966767313NN

Label: 17, with a score of: 0.363243426472551
WordScorePoS
gdp0NN
greenhouse0NN
gas0NN
emission0NN
country0NN
income0.017987180284335NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

Label: 10, with a score of: 0.294038674694994
WordScorePoS
gdp0NN
greenhouse0NN
gas0NN
emission0NN
country0NN
income0.166475342664294NN
building0NN
sustainability0NN
world0.00276879546803864NN

Label: 7, with a score of: 0.451756739853801
WordScorePoS
gdp0NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN
country0NN
income0.0449973847138318NN
building0NN
sustainability0NN
world0.00261936653839306NN

Carbon Dioxide Emissions versus Urbanization Carbon Dioxide Emissions metric tons capita United States Germany South Africa United Kingdom Japan France Sweden China Mexico Korea Rep

Label: 13, with a score of: 0.84438411685023
WordScorePoS
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
urbanization0NN
ton0NN
capita0NN
unite0.0828215106044499VB
south0RB
africa0IN

Label: 11, with a score of: 0.382069190938776
WordScorePoS
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
urbanization0.146277152173395NN
ton0NN
capita0.04477822029736NN
unite0VB
south0RB
africa0IN

India Brazil Nigeria Urban Population of total Source Author calculations see data in Annex

Label: 11, with a score of: 0.747381930328172
WordScorePoS
urban0.27622591898666JJ
population0.0194583992186006NN
total0NN
source0NN
datum0NN

Label: 10, with a score of: 0.341063016949848
WordScorePoS
urban0.0488696285210061JJ
population0.0860640399817412NN
total0NN
source0NN
datum0NN

Label: 6, with a score of: 0.321594387807808
WordScorePoS
urban0JJ
population0.0363595928905615NN
total0.0295155485671997NN
source0.0613562156822703NN
datum0NN

for only percent of the world total GDP but emit percent of total greenhouse gases

Label: 7, with a score of: 0.723991733275891
WordScorePoS
world0.00261936653839306NN
total0.0528748274225028NN
gdp0NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN

Label: 12, with a score of: 0.462012498390741
WordScorePoS
world0.00464349782489754NN
total0.0468670845659596NN
gdp0NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN

Label: 13, with a score of: 0.386241193194354
WordScorePoS
world0.00351929966767313NN
total0NN
gdp0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN

Greater carbon efficiency can and should be pursued as these economies continue to grow

Label: 13, with a score of: 0.625628779661577
WordScorePoS
carbon0.0559964069957828NN
pursue0VB
economy0.0839946104936743NN
continue0.0403047273067529VB
grow0.0171695137813101VB

Label: 7, with a score of: 0.396138874368115
WordScorePoS
carbon0.0625160381065121NN
pursue0VB
economy0.0625160381065121NN
continue0VB
grow0VB

Label: 11, with a score of: 0.361271262381811
WordScorePoS
carbon0.0373517915079663NN
pursue0VB
economy0NN
continue0.0537696560159885VB
grow0.0229054731708393VB

Label: 8, with a score of: 0.354154419264591
WordScorePoS
carbon0NN
pursue0VB
economy0.0821985033584295NN
continue0.0295821503613099VB
grow0VB

Figure shows another simple analysis of urban sustainability based on different pair of indicators in this case the per capita emissions of countries versus their level of urbanization

Label: 11, with a score of: 0.827345107124542
WordScorePoS
figure0NN
simple0JJ
urban0.27622591898666JJ
sustainability0NN
base0NN
capita0.04477822029736NN
emission0.0373517915079663NN
country0NN
level0.00692515656684925NN
urbanization0.146277152173395NN

Label: 13, with a score of: 0.38413419091819
WordScorePoS
figure0NN
simple0JJ
urban0JJ
sustainability0NN
base0NN
capita0NN
emission0.19598742448524NN
country0NN
level0.0415277415054218NN
urbanization0NN

Some interesting patterns appear Brazil for example has the greatest rate of urbanization but relatively low emissions growth

Label: 13, with a score of: 0.531215533922403
WordScorePoS
pattern0.0414107553022249NN
rate0NN
urbanization0NN
emission0.19598742448524NN
growth0NN

Label: 8, with a score of: 0.466698131426983
WordScorePoS
pattern0NN
rate0NN
urbanization0NN
emission0NN
growth0.208565600657229NN

Label: 11, with a score of: 0.410898463706132
WordScorePoS
pattern0NN
rate0NN
urbanization0.146277152173395NN
emission0.0373517915079663NN
growth0NN

Label: 12, with a score of: 0.307389598841285
WordScorePoS
pattern0.0819583881728876NN
rate0NN
urbanization0NN
emission0.0554128417527413NN
growth0NN

Overall the trend is consistent with previous analysis public policies play large role in decreasing carbon intensity

Label: 7, with a score of: 0.497272584518223
WordScorePoS
consistent0JJ
previous0JJ
public0NN
policy0NN
decrease0NN
carbon0.0625160381065121NN
intensity0.122412709674631NN

Label: 11, with a score of: 0.490912655534824
WordScorePoS
consistent0JJ
previous0JJ
public0.0583751976558017NN
policy0.0136980135278128NN
decrease0.0731385760866977NN
carbon0.0373517915079663NN
intensity0NN

Label: 17, with a score of: 0.376914951016795
WordScorePoS
consistent0.036961556179878JJ
previous0JJ
public0.0390556786919513NN
policy0.0641521854453136NN
decrease0NN
carbon0NN
intensity0NN

Label: 13, with a score of: 0.325603720917025
WordScorePoS
consistent0JJ
previous0.0548233071065585JJ
public0NN
policy0.0102677744436108NN
decrease0NN
carbon0.0559964069957828NN
intensity0NN

Other frameworks can also be used to understand differences among cities

Label: 11, with a score of: 0.991254382846226
WordScorePoS
framework0.0194583992186006NN
city0.607697021770652NN

For instance Bai and Imura compare East Asian cities by describing four sequential stages in the evolving urban environment the poverty stage the industrial pollution stage the mass consumption stage and the eco city stage

Label: 11, with a score of: 0.878329379590207
WordScorePoS
compare0VB
city0.607697021770652NN
stage0NN
urban0.27622591898666JJ
environment0.0268848280079943NN
poverty0.0164178645080222NN
industrial0JJ
pollution0.0315914058171786NN
consumption0.08955644059472NN

Label: 9, with a score of: 0.340213481580627
WordScorePoS
compare0VB
city0NN
stage0.0602029956716497NN
urban0JJ
environment0.0221298700466866NN
poverty0NN
industrial0.318320101004075JJ
pollution0NN
consumption0NN

Label: 12, with a score of: 0.237357557810391
WordScorePoS
compare0VB
city0NN
stage0NN
urban0JJ
environment0.079769385071606NN
poverty0.0121782716594324NN
industrial0JJ
pollution0.0234335422829798NN
consumption0.332151194779746NN

Label: 8, with a score of: 0.148098694019188
WordScorePoS
compare0VB
city0NN
stage0NN
urban0JJ
environment0.0591643007226199NN
poverty0.0722601961736204NN
industrial0JJ
pollution0NN
consumption0.147812295323721NN

Label: 1, with a score of: 0.143183365409399
WordScorePoS
compare0VB
city0NN
stage0NN
urban0JJ
environment0NN
poverty0.269969049497485NN
industrial0JJ
pollution0NN
consumption0NN

The authors argue that for particular city at given time environmental issues related to poverty production or consumption gain dominance until another group of issues becomes prominent in the succeeding stage of development

Label: 11, with a score of: 0.690405762650661
WordScorePoS
city0.607697021770652NN
time0NN
environmental0.0537696560159885JJ
issue0NN
related0.0112376090361851JJ
poverty0.0164178645080222NN
production0NN
consumption0.08955644059472NN
gain0.04477822029736NN
stage0NN
development0.0100241617629471NN

Label: 12, with a score of: 0.527780221142266
WordScorePoS
city0NN
time0.0169905820958638NN
environmental0.0598270388037045JJ
issue0NN
related0.00833571598658786JJ
poverty0.0121782716594324NN
production0.118934074671047NN
consumption0.332151194779746NN
gain0.0664302389559493NN
stage0NN
development0.022306853314744NN

Label: 1, with a score of: 0.289206239850994
WordScorePoS
city0NN
time0NN
environmental0.0442083771593746JJ
issue0NN
related0.0184786920895884JJ
poverty0.269969049497485NN
production0NN
consumption0NN
gain0NN
stage0NN
development0.0164833460638534NN

Label: 8, with a score of: 0.263363917205216
WordScorePoS
city0NN
time0NN
environmental0.0295821503613099JJ
issue0NN
related0.0123650647908625JJ
poverty0.0722601961736204NN
production0.0504071033101074NN
consumption0.147812295323721NN
gain0NN
stage0NN
development0.0055149369084567NN

The eco city stage assumes that as the level of economic development increases citizens will adopt more resource efficient lifestyles and develop PART II THE PATH TO SUSTAINABILITY greater environmental consciousness

Label: 11, with a score of: 0.780219607535775
WordScorePoS
city0.607697021770652NN
stage0NN
level0.00692515656684925NN
economic0.0389167984372011JJ
development0.0100241617629471NN
increase0.00323105002784081NN
adopt0.0229054731708393VB
resource0.0449504361447405NN
lifestyle0NN
develop0VB
sustainability0NN
environmental0.0537696560159885JJ

Label: 8, with a score of: 0.188673390984378
WordScorePoS
city0NN
stage0NN
level0.00761994916892276NN
economic0.128463821595706JJ
development0.0055149369084567NN
increase0.00711043475673939NN
adopt0VB
resource0.0123650647908625NN
lifestyle0NN
develop0VB
sustainability0NN
environmental0.0295821503613099JJ

Label: 12, with a score of: 0.358971612230152
WordScorePoS
city0NN
stage0NN
level0.00513687014008325NN
economic0.0433009429988926JJ
development0.022306853314744NN
increase0.00958677785775002NN
adopt0.0169905820958638VB
resource0.0416785799329393NN
lifestyle0.122937582259331NN
develop0VB
sustainability0.0409791940864438NN
environmental0.0598270388037045JJ

For the urban areas that we have examined this stage may be represented best by Quadrant III cities

Label: 11, with a score of: 0.99226202658144
WordScorePoS
urban0.27622591898666JJ
stage0NN
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city0.607697021770652NN

oping world tools designed to assess environmental quality should be expanded to measure how prepared these cities are for climate change

Label: 13, with a score of: 0.66000217623403
WordScorePoS
world0.00351929966767313NN
tool0NN
environmental0JJ
quality0NN
expand0.0201523636533764VB
measure0.0710409787438328NN
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climate0.291881734282273NN
change0.402565546215053NN

Label: 11, with a score of: 0.637961820956137
WordScorePoS
world0.00626002771693937NN
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environmental0.0537696560159885JJ
quality0.0136980135278128NN
expand0.0268848280079943VB
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city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN

Analyses and categorizations such as these can be made more comprehensive and accurate as urban data collection becomes more institutionalized and more standardized

Label: 11, with a score of: 0.892210080263212
WordScorePoS
urban0.27622591898666JJ
datum0NN
collection0NN

Label: 17, with a score of: 0.396825579895
WordScorePoS
urban0JJ
datum0.0739231123597559NN
collection0.048933054487825NN

Given the wealth of information that has been extracted here with even an extremely limited dataset the Large Urban Areas Compendium can be expected to contribute even more to efforts at understanding the drivers of sustainability and developing broader and deeper typology of sustainable cities

Label: 11, with a score of: 0.944395286802823
WordScorePoS
information0NN
limited0JJ
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expect0VB
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sustainable0.00782503464617421JJ
city0.607697021770652NN

While the consequences of climate change are increasingly evident in cities across the world the process of assessing and forecasting the risks for an individual urban area is complex and accompanied by considerable uncertainty Box

Label: 11, with a score of: 0.785770832061813
WordScorePoS
climate0.0229054731708393NN
change0.0315914058171786NN
city0.607697021770652NN
world0.00626002771693937NN
process0NN
risk0.0631828116343571NN
individual0JJ
urban0.27622591898666JJ

Label: 13, with a score of: 0.544163210629484
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
city0NN
world0.00351929966767313NN
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Box lists some of the most vulnerable cities based on multiple different ranking studies

Label: 11, with a score of: 0.950222693612621
WordScorePoS
vulnerable0.0458109463416786JJ
city0.607697021770652NN
base0NN
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As yet however there is no reliable internationally accepted common metric that would establish which cities are most at risk and enable governments to track progress toward urban resilience and adaptation

Label: 11, with a score of: 0.943667051680936
WordScorePoS
reliable0JJ
internationally0RB
common0.055245183797332JJ
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city0.607697021770652NN
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enable0.0373517915079663VB
government0NN
progress0NN
urban0.27622591898666JJ
resilience0.0373517915079663NN
adaptation0.04477822029736NN

Label: 7, with a score of: 0.126086188445036
WordScorePoS
reliable0.149891440995783JJ
internationally0RB
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city0NN
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The Case for an Urban Resilience Index An integrated urban risk metric would standardize the procedures requirements and steps to measure hazard exposure vulnerability and adaptive capacity as well as the economic valuation of projected damages and losses

Label: 11, with a score of: 0.720387566939414
WordScorePoS
urban0.27622591898666JJ
resilience0.0373517915079663NN
integrate0.0537696560159885VB
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capacity0.0112376090361851NN
economic0.0389167984372011JJ
project0NN
loss0.0315914058171786NN

Label: 8, with a score of: 0.187448292079268
WordScorePoS
urban0JJ
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hazard0NN
exposure0NN
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adaptive0JJ
capacity0.0123650647908625NN
economic0.128463821595706JJ
project0NN
loss0NN

Label: 1, with a score of: 0.505946907032752
WordScorePoS
urban0JJ
resilience0.0614198493690006NN
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risk0.0519476926891779NN
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hazard0NN
exposure0.120266261535276NN
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adaptive0JJ
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economic0.0959899298575892JJ
project0NN
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Such standardized Efficiency and pollution are not the only factors that need to be monitored and managed in sustainable city

Label: 11, with a score of: 0.948731443178383
WordScorePoS
pollution0.0315914058171786NN
monitor0NN
manage0VB
sustainable0.00782503464617421JJ
city0.607697021770652NN

As adaptation becomes more important in fast growing and vulnerable cities of the devel BOX Forecasting Climate Hazards Photo Julianne Baker Gallegos World Bank While certain types of hazards such as earthquakes can be forecasted with some accuracy at least in terms of location if not of timing others are even more difficult to forecast

Label: 11, with a score of: 0.738658060177116
WordScorePoS
adaptation0.04477822029736NN
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vulnerable0.0458109463416786JJ
city0.607697021770652NN
climate0.0229054731708393NN
hazard0NN
world0.00626002771693937NN
bank0NN
type0NN
term0NN
location0NN

Label: 13, with a score of: 0.498619216230921
WordScorePoS
adaptation0.0671298309154199NN
grow0.0171695137813101VB
vulnerable0.0171695137813101JJ
city0NN
climate0.291881734282273NN
hazard0.0548233071065585NN
world0.00351929966767313NN
bank0NN
type0NN
term0NN
location0NN

Label: 17, with a score of: 0.236136247343763
WordScorePoS
adaptation0NN
grow0VB
vulnerable0JJ
city0NN
climate0NN
hazard0NN
world0.00418824502414673NN
bank0.036961556179878NN
type0NN
term0.149793392961411NN
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For instance sea level rise occurs at different rates in different parts of the globe and is highly dependent on among other things how much and how fast the Arctic and Greenland ice caps melt

Label: 13, with a score of: 0.952116539060714
WordScorePoS
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
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rate0NN
highly0RB
arctic0.0414107553022249JJ
ice0.274116535532792NN
melt0.109646614213117VB

The frequency and intensity of high precipitation events which increasingly are triggering devastating floods in many cities can be predicted from global climate models via downscaling techniques but generally with significant uncertainty

Label: 11, with a score of: 0.768706699360055
WordScorePoS
intensity0NN
event0NN
city0.607697021770652NN
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global0.00501208088147356JJ
climate0.0229054731708393NN

Label: 13, with a score of: 0.514820213223761
WordScorePoS
intensity0NN
event0.0414107553022249NN
city0NN
predict0.0548233071065585VB
global0.0375696197697663JJ
climate0.291881734282273NN

Label: 7, with a score of: 0.317574424155613
WordScorePoS
intensity0.122412709674631NN
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city0NN
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global0.0251662980592747JJ
climate0.115011305411453NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD steps would also specify the probabilistic risk assessment and climate change downscaling techniques to be applied

Label: 13, with a score of: 0.849318528536665
WordScorePoS
building0NN
sustainability0NN
world0.00351929966767313NN
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climate0.291881734282273NN
change0.402565546215053NN

Label: 17, with a score of: 0.232527470941113
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
risk0NN
climate0NN
change0NN

When such metric is established and widely adopted it will help to focus the attention of local national and international policy makers on urban risks presumably triggering more preemptive action and greater financing

Label: 11, with a score of: 0.753822914100395
WordScorePoS
establish0VB
widely0RB
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urban0.27622591898666JJ
risk0.0631828116343571NN
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Label: 10, with a score of: 0.388122784082837
WordScorePoS
establish0VB
widely0RB
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Urban risk and resilience are complex and multidimensional and it has proven extremely difficult thus far to reduce these issues to few indicators and by extension to an aggregate index that can provide comprehensive assessment of resilience

Label: 11, with a score of: 0.816601792056225
WordScorePoS
urban0.27622591898666JJ
risk0.0631828116343571NN
resilience0.0373517915079663NN
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issue0NN
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provide0.0269743071558856VB

One major effort by the World Bank to address this gap has been the development of the Urban Risk Assessment methodology Box

Label: 11, with a score of: 0.798525333448597
WordScorePoS
major0JJ
world0.00626002771693937NN
bank0NN
gap0NN
development0.0100241617629471NN
urban0.27622591898666JJ
risk0.0631828116343571NN

Label: 2, with a score of: 0.318367811242676
WordScorePoS
major0JJ
world0.0124838525528648NN
bank0.0881366997038154NN
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development0.0159922815774215NN
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However Urban Risk Assessment is an approach for the detailed specification of where and how many people are vulnerable to natural hazards and identification of susceptible urban infrastructure

Label: 11, with a score of: 0.858828275831666
WordScorePoS
urban0.27622591898666JJ
risk0.0631828116343571NN
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people0.0161552501392041NNS
vulnerable0.0458109463416786JJ
natural0.0229054731708393JJ
hazard0NN
infrastructure0.0194583992186006NN

Label: 9, with a score of: 0.251482090441992
WordScorePoS
urban0JJ
risk0NN
approach0NN
people0.0186171553883251NNS
vulnerable0JJ
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infrastructure0.192202909044225NN

It does not generate comparable standardized index that condenses the multiple dimensions of disaster and climate risk and resilience

Label: 1, with a score of: 0.603630376695501
WordScorePoS
generate0VB
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dimension0.181686110904276NN
disaster0.0519476926891779NN
climate0.0376648791150643NN
risk0.0519476926891779NN
resilience0.0614198493690006NN

Label: 13, with a score of: 0.539125606150299
WordScorePoS
generate0VB
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disaster0.0236803262479443NN
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Label: 11, with a score of: 0.441577216289464
WordScorePoS
generate0VB
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The Large Urban Areas Compendium presented above will contribute to the development of comprehensive urban resilience index

Label: 11, with a score of: 0.937341625615626
WordScorePoS
urban0.27622591898666JJ
contribute0VB
development0.0100241617629471NN
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Eventually robust analytics can be carried out to arrive at typology of urban risk that would enable comparisons across different cities

Label: 11, with a score of: 0.989444272366157
WordScorePoS
urban0.27622591898666JJ
risk0.0631828116343571NN
enable0.0373517915079663VB
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Such typology would be useful in prioritizing the optimal types of interventions according to city type

Label: 11, with a score of: 0.963293775488537
WordScorePoS
type0NN
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city0.607697021770652NN

However as with typologies along the other dimensions of sustainability any initiative to develop an urban resilience typology will be fruitful only if data is collected and updated on regular basis

Label: 11, with a score of: 0.645000017834021
WordScorePoS
dimension0NN
sustainability0NN
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urban0.27622591898666JJ
resilience0.0373517915079663NN
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Label: 1, with a score of: 0.5000462196898
WordScorePoS
dimension0.181686110904276NN
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resilience0.0614198493690006NN
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Label: 17, with a score of: 0.40475657022272
WordScorePoS
dimension0NN
sustainability0.036961556179878NN
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datum0.0739231123597559NN
regular0JJ
basis0.048933054487825NN

Label: 10, with a score of: 0.334118322820188
WordScorePoS
dimension0.0488696285210061NN
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Which Cities are Most at Risk from Climate Change

Label: 11, with a score of: 0.687499002159073
WordScorePoS
city0.607697021770652NN
risk0.0631828116343571NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.658184642871894
WordScorePoS
city0NN
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climate0.291881734282273NN
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BOX Various in depth studies over the past decade have assessed the climaterelated risks facing individual cities worldwide and some global assessments have provided overviews of urban risk across multiple cities

Label: 11, with a score of: 0.977515767669023
WordScorePoS
study0NN
decade0.055245183797332NN
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face0NN
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worldwide0RB
global0.00501208088147356JJ
urban0.27622591898666JJ
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As each of these reviews uses different methodology the results are difficult to compare

Label: 5, with a score of: 0.594028961014468
WordScorePoS
review0.0688661930304451NN
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Label: 6, with a score of: 0.589426898683525
WordScorePoS
review0NN
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In addition some have focused on the risk to resident populations others on the risk to economic assets

Label: 8, with a score of: 0.413060541919878
WordScorePoS
addition0NN
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population0.0428212738652355NN
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asset0NN

Label: 10, with a score of: 0.463289156444545
WordScorePoS
addition0NN
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risk0NN
population0.0860640399817412NN
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asset0.0488696285210061NN

Label: 11, with a score of: 0.431941883114498
WordScorePoS
addition0NN
focus0.0373517915079663NN
risk0.0631828116343571NN
population0.0194583992186006NN
economic0.0389167984372011JJ
asset0NN

Label: 1, with a score of: 0.388751514624093
WordScorePoS
addition0NN
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risk0.0519476926891779NN
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economic0.0959899298575892JJ
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Nevertheless to gain broad idea of the cities most at risk members of the Partnership for Sustainable cities compared the results of multiple global rankings of cities Annex each conducted according to its own criteria

Label: 11, with a score of: 0.963115817195078
WordScorePoS
gain0.04477822029736NN
idea0.0731385760866977NN
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partnership0NN
sustainable0.00782503464617421JJ
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Label: 17, with a score of: 0.225393229161868
WordScorePoS
gain0NN
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partnership0.3914644359026NN
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multiple0.048933054487825JJ
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The top cities appearing in these rankings were 'Dhaka 'Kolkata 'Beijing 'Manila 'Chittagong 'Mexico City 'Istanbul 'Mumbai 'Jakarta 'Shanghai While this list should be considered provisional for the methodological reasons just described it can help focus the attention of local national and international policy makers on the urgent need to address risks in these cities

Label: 11, with a score of: 0.981024260350165
WordScorePoS
city0.607697021770652NN
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In general the cities considered most at risk are located in Southeast Asia

Label: 11, with a score of: 0.985915996589677
WordScorePoS
city0.607697021770652NN
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Among the top the only non Asian city is Mexico City

Label: 11, with a score of: 0.997380969185671
WordScorePoS
city0.607697021770652NN

Photo Arne Hoel World Bank Urban Risk Assessment An ancillary objective of the URA is to better position cities to absorb and allocate discrete adaptation funds should they be available

Label: 11, with a score of: 0.964921965185529
WordScorePoS
world0.00626002771693937NN
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There are no direct linkages between city level actions and National Adaptation BOX To date the predominant response to disasters both within city governments and international agencies has largely been reactive

Label: 11, with a score of: 0.965218006572259
WordScorePoS
direct0.04477822029736JJ
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Given the significant impact that natural hazards and climate change will have on urban investments increasing priority should be placed on proactive adaptive planning to reduce and manage the potential for disasters and climate change

Label: 13, with a score of: 0.837102516330327
WordScorePoS
impact0.0437569550806737NN
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Label: 11, with a score of: 0.342516896969633
WordScorePoS
impact0.0194583992186006NN
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With this recognition the value of identifying diagnosing and mapping high risk areas is gaining visibility and importance

Label: 11, with a score of: 0.629250033825023
WordScorePoS
identify0VB
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This has resulted in proliferation of city risk and hazard assessments without common approach

Label: 11, with a score of: 0.96749676084233
WordScorePoS
result0NN
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The Urban Risk Assessment URA seeks to strengthen coherence and consensus minimize duplicative efforts and bring convergence to related work undertaken across the World Bank and key partner organizations

Label: 11, with a score of: 0.694051318740434
WordScorePoS
urban0.27622591898666JJ
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Label: 17, with a score of: 0.35702653853926
WordScorePoS
urban0JJ
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The objective is to move towards common cost effective approach for specifying where and how many people are vulnerable to natural hazards in addition to identifying susceptible infrastructure that if damaged would have knock on detrimental effects on the urban population

Label: 11, with a score of: 0.56630538174806
WordScorePoS
objective0NN
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urban0.27622591898666JJ
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Label: 9, with a score of: 0.410507099740704
WordScorePoS
objective0.0368585655748969NN
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Label: 12, with a score of: 0.352583101895252
WordScorePoS
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Label: 10, with a score of: 0.300428095566349
WordScorePoS
objective0NN
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vulnerable0.0202620733250388JJ
natural0JJ
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urban0.0488696285210061JJ
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Programs of Action NAPA and no funding schemes in place to finance their implementation

Label: 0, with a score of: 1

When compared to other sectors such as forestry or agriculture that have typically received sizeable allocations for climate adaptation funding cities have lacked necessary mechanisms and tools to begin sustainably addressing climate change and disaster management

Label: 13, with a score of: 0.684817520605024
WordScorePoS
compare0.0414107553022249VB
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Label: 11, with a score of: 0.569817157793466
WordScorePoS
compare0VB
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WordScorePoS
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WordScorePoS
compare0VB
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The URA is based upon four principal building blocks to improve the understanding of urban risk historical incidence of hazards geospatial data institutional mapping and community participation

Label: 11, with a score of: 0.664507183279864
WordScorePoS
base0NN
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Label: 17, with a score of: 0.447222784184883
WordScorePoS
base0.017987180284335NN
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historical0JJ
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The URA is structured to allow flexibility in how it is applied depending on available resources and institutional capacity of given city

Label: 11, with a score of: 0.904674942289012
WordScorePoS
structure0NN
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resource0.0449504361447405NN
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Through phased approach linked to complexity and required investment city managers may select series of subcomponents from each building block that individually and collectively enhance the understanding of urban risk

Label: 11, with a score of: 0.921743124341716
WordScorePoS
approach0NN
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Label: 17, with a score of: 0.274679719364247
WordScorePoS
approach0NN
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The URA lays the groundwork for the definition of plan for strategic collaboration across city governments the private sector and development agencies to begin benchmarking their own progress towards the reduction of urban vulnerability

Label: 11, with a score of: 0.906497450187149
WordScorePoS
plan0.0583751976558017NN
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government0NN
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Source Reprinted from World Bank

Label: 2, with a score of: 0.737559496131694
WordScorePoS
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Label: 6, with a score of: 0.426402661922482
WordScorePoS
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Label: 17, with a score of: 0.33813808928791
WordScorePoS
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD What Should Resilience Metric Include

Label: 17, with a score of: 0.709658249359869
WordScorePoS
building0.149940347231585NN
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Label: 11, with a score of: 0.439392097991041
WordScorePoS
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Label: 16, with a score of: 0.407734336484241
WordScorePoS
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average

Label: 13, with a score of: 0.748820377957753
WordScorePoS
average0.1656430212089NN

Label: 10, with a score of: 0.662772993984523
WordScorePoS
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For all hazard types climatological geophysical hydrological and meteorological the vast majority of victims were in Asia

Label: 0, with a score of: 1

During this period Africa accounted for percent of the victims of climatological hazards in this case drought

Label: 2, with a score of: 0.595763438805461
WordScorePoS
period0.0440683498519077NN
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Label: 12, with a score of: 0.509150121081677
WordScorePoS
period0.0409791940864438NN
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However across all types of hazards the Americas sustained the largest share of economic damage despite accounting for only percent of the world victims of geophysical hazards and even lower proportions for the other types

Label: 8, with a score of: 0.335938772992527
WordScorePoS
type0NN
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Label: 9, with a score of: 0.511662065500511
WordScorePoS
type0NN
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Label: 5, with a score of: 0.508687736866956
WordScorePoS
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While this region has fewer people at risk it has more economic assets

Label: 1, with a score of: 0.709869370980452
WordScorePoS
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Label: 8, with a score of: 0.267371109591949
WordScorePoS
region0NN
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Label: 3, with a score of: 0.385554582995044
WordScorePoS
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Label: 10, with a score of: 0.303371788356235
WordScorePoS
region0NN
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In order to be comprehensive any urban resilience metric or index would primarily need to integrate natural disaster and climate risks

Label: 11, with a score of: 0.777045387396324
WordScorePoS
urban0.27622591898666JJ
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Label: 13, with a score of: 0.466598727267855
WordScorePoS
urban0JJ
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It needs to recognize the full extent of certain climate impacts that occur over large areas and accumulate over time

Label: 13, with a score of: 0.79687482208946
WordScorePoS
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These can be in the form of large numbers of widespread but localized disasters associated with sea level rise drought and flooding due to storm surges for example

Label: 13, with a score of: 0.616392680460336
WordScorePoS
form0NN
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Label: 16, with a score of: 0.33655369421213
WordScorePoS
form0.158049144643351NN
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Label: 11, with a score of: 0.294546674619274
WordScorePoS
form0NN
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Label: 6, with a score of: 0.358373926646415
WordScorePoS
form0NN
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Label: 1, with a score of: 0.312874664384185
WordScorePoS
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While possibly accounting for only small proportion of overall disaster mortality in comparison to geophysical hazards like earthquakes extensive climate risks can significantly damage housing and local infrastructure particularly in low income communities

Label: 11, with a score of: 0.498758204320255
WordScorePoS
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Label: 13, with a score of: 0.416288914727676
WordScorePoS
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Label: 3, with a score of: 0.370184282684661
WordScorePoS
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Label: 9, with a score of: 0.364535338101496
WordScorePoS
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Label: 10, with a score of: 0.233909493913088
WordScorePoS
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Label: 7, with a score of: 0.315320097161568
WordScorePoS
accounting0.0924643738905717NN
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Furthermore in addition to the hazards directly threatening city those that may affect it indirectly such as the future yield of rural watersheds from which cities draws water resources need to be part of the assessment of urban risk

Label: 11, with a score of: 0.905444494370085
WordScorePoS
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Label: 6, with a score of: 0.298386306300844
WordScorePoS
addition0NN
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Label: 2, with a score of: 0.191420268026853
WordScorePoS
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Label: 12, with a score of: 0.13489097120612
WordScorePoS
addition0NN
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Assessment of exposure to hazards will involve detailed data describing city precise elevation geological profile air quality the hydrology of natural waterways and drainage systems ecosystems the location and characteristics of infrastructure the utility systems and the spatial distribution of residential commercial and productive areas

Label: 11, with a score of: 0.726006145031861
WordScorePoS
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Label: 9, with a score of: 0.247191722605798
WordScorePoS
exposure0NN
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Label: 15, with a score of: 0.14274940992628
WordScorePoS
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Label: 12, with a score of: 0.350960682836713
WordScorePoS
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Such analytics generally captured in the form of GIS layers and maps need to be accompanied by detailed assessments of the vulnerability of city assets

Label: 11, with a score of: 0.862264470276644
WordScorePoS
capture0NN
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Label: 16, with a score of: 0.339462119212114
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For instance geological micro zoning can assess which parts of the city are most exposed to earthquakes but only the detailed analysis of the structural characteristics of buildings in those zones will determine their vulnerability in case of an event

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WordScorePoS
micro0JJ
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Label: 17, with a score of: 0.24653750769916
WordScorePoS
micro0JJ
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To present complete picture of risk an urban resilience index must address the exposure of both the population and economic assets

Label: 11, with a score of: 0.648667288868727
WordScorePoS
complete0JJ
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Label: 8, with a score of: 0.255338813718219
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Label: 1, with a score of: 0.491378031732785
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Label: 10, with a score of: 0.427957280635313
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Figure shows the regional distribution of the number of hazard victims as well as the shares of economic damages Averages Source Adapted from Bigio

Label: 10, with a score of: 0.499532166403817
WordScorePoS
figure0NN
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Label: 13, with a score of: 0.46532279155716
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Label: 8, with a score of: 0.30718758841392
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Label: 1, with a score of: 0.349701530618942
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Label: 9, with a score of: 0.338764940030262
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Americas Asia Europe Oceania

Label: 5, with a score of: 1
WordScorePoS
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Climatological

Label: 0, with a score of: 1

Geophysical

Label: 0, with a score of: 1

Hydrological Disaster Type

Label: 11, with a score of: 0.784854417278581
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Label: 5, with a score of: 0.342181263561961
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Meteorological

Label: 0, with a score of: 1

All Types Victims by Region Annual Ave of global total Impacts of Natural Hazards by Region Africa Damages by Region Annual Ave of global total FIG

Label: 1, with a score of: 0.672877754461661
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Label: 12, with a score of: 0.314444036343634
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Climatological

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Geophysical

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Hydrological Disaster Type

Label: 11, with a score of: 0.784854417278581
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Label: 5, with a score of: 0.342181263561961
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Meteorological

Label: 0, with a score of: 1

All Types PART II THE PATH TO SUSTAINABILITY Another important component of urban risk assessment that should be integrated in standardized urban risk metric is the economic valuation of potential damages and losses from projected natural disasters and climate change impacts

Label: 11, with a score of: 0.677050689285544
WordScorePoS
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Label: 13, with a score of: 0.548117988003967
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Label: 8, with a score of: 0.101147375954092
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Economic valuation techniques draw from environmental economics and cost benefit analysis methodologies to project the cumulative value over the assessment period and calculate net present value of the aggregated amounts based on accepted discount rates

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As the damages and losses can be attributed to specific vulnerabilities and risks the costs of mitigation or remedial actions can be calculated

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This will help policy makers understand which measures are most cost effective and will provide return in terms of avoided damages and losses greater than their costs

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However as with urban resilience metrics in general standardized methodology for economic valuation has not yet been established

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Finally risk management mechanisms have to be assessed

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This entails review of the technical and governance measures that sub national or national government agencies have taken or can take to address the risks identified through the previous steps

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Such measures include setting up and managing early warning systems improving hazard forecasting capabilities and public information systems educating and mobilizing citizens via community emergency plans coordinating emergency responses across institutions and mobilizing of technical and financial capabilities for urban resilience and adaptation

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All these contribute to the adaptive capacity of the city

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While scientific analysis can measure hazards exposure and vulnerability albeit with various degrees of uncertainty the adaptive capacity of given city and its institutions will be assessed mostly through qualitative evaluations of the response mechanisms

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The results are likely to be based on expert judgment rather than solely on data driven indicators

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There are of course established methods for such adaptive capacity assessments but their conclusions are very much related to the institu tional and cultural context in which they are carried out

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The comparison of adaptive capacity across number of cities via common metric raises methodological issues that have not yet been resolved

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At the Partnership Toward Sustainable Cities workshop June representatives from NGOs corporations government agencies and universities described more than two dozen comparative urban tools

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These included systems for gathering and classifying data indicators indices and rating schemes analytical frameworks for measuring urban characteristics and impacts communication tools for presenting complex datasets and analyses and funding and development strategies

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Further Reading Annex describes Siemens Green City Index series in detail

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Annex gives additional background on the GCIF

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Annex outlines the data needed for an abbreviated urban metabolism study designed for cities with limited resources or institutional capacity

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Annex diagrams the metabolic flows of cities

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Annex lists the cities used for the initial release of the Large Urban Areas Compendium and for the city typology discussed above

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Annex shows examples of Large Urban Areas Compendium data from three cities

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Annex applies multi hazard risk assessment the largest urban areas

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Annex reviews existing rankings of the world most at risk cities

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Annex details the urban data that Earth observation satellites can provide and its uses in planning and disaster risk management

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PART III The Role of Institutions and Partnerships Photo Curt Carnemark World Bank PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS Governance and Implementation Key Messages ' Strong institutions and partnerships among the public sector private sector and civil society are needed in order to adopt multi sectoral policies for sustainable green urban growth

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' Capacity building in local governments and enabling policies at the national level are both important to support sustainable cities

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' Public private partnerships are opening the provision of public services to the private sector

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However the public authority must create the enabling policy environment and regulatory framework that protects the interests of both citizens and investors

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' Transnational municipal networks allow cities around the world to collaborate on innovative approaches for urban sustainability

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Agreements among cities can circumvent deadlocked international policy negotiations

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' Civil society is emerging as key stakeholder in implementing participatory processes in urban governance

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At the same time information and communications technology is opening vast new horizons for public participation

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time0NN
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Label: 17, with a score of: 0.443143948168161
WordScorePoS
time0NN
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' Despite the existence of wide array of options for making urban systems sustainable but ideas tools and metrics do not create sustainable cities by themselves

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WordScorePoS
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The key to changing the situation on the ground is institutions and their interactions see Box

Label: 13, with a score of: 0.712816402624665
WordScorePoS
key0NN
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Label: 16, with a score of: 0.486012477301971
WordScorePoS
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BOX Case Study Institutions and Adaptation in Louisiana and the Netherlands Louisiana and the Netherlands had very different reactions to local rises in sea level during the th century and the contrast shows the importance of institutions in adapting to the impacts of climate change

Label: 13, with a score of: 0.8572727792431
WordScorePoS
study0NN
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change0.402565546215053NN

Label: 16, with a score of: 0.399765610647866
WordScorePoS
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In the case of Louisiana risks were addressed ex post with levees being reinforced only after disasters had occurred

Label: 11, with a score of: 0.641511238710264
WordScorePoS
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Label: 16, with a score of: 0.471071240935462
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Label: 1, with a score of: 0.301393259365978
WordScorePoS
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In the Netherlands on the other hand the Delta Commission institutionalized risk management by reinforcing the seawalls on regular basis among other measures

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WordScorePoS
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Label: 11, with a score of: 0.409403111554558
WordScorePoS
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Thus the Netherlands successful strategy since is arguably due more to institutions like the Delta Commission than to physical protections such as seawalls

Label: 16, with a score of: 0.645847831151473
WordScorePoS
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Label: 1, with a score of: 0.515299346792741
WordScorePoS
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Label: 10, with a score of: 0.388731670383931
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Photo RF BUILDING SUSTAINABILITY IN AN URBANIZING WORLD These institutions can belong to the public sector international agencies bureaucratic line agencies elected local government units the private sector businesses developers investors or civil society transnational municipal networks NGOs

Label: 17, with a score of: 0.588494338884075
WordScorePoS
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Label: 16, with a score of: 0.506705911339504
WordScorePoS
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The issues facing cities span all these sectors and multisectoral policies are needed

Label: 11, with a score of: 0.904104424727706
WordScorePoS
issue0NN
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For such policies to be successful cities have to embrace broad participation in decision making and use reliable institutions or partnerships for implementation Box

Label: 11, with a score of: 0.605983389329103
WordScorePoS
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Label: 17, with a score of: 0.583774860284445
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Label: 16, with a score of: 0.386114133991182
WordScorePoS
policy0.0201317944152378NN
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Label: 7, with a score of: 0.146173880199123
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BOX For example land use urban form and urban planning are critical to transport related energy consumption and for adaptation planning

Label: 11, with a score of: 0.73144059635605
WordScorePoS
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adaptation0.04477822029736NN

Label: 12, with a score of: 0.408089545146336
WordScorePoS
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Label: 7, with a score of: 0.394420306945233
WordScorePoS
land0.0325677023224526NN
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Label: 8, with a score of: 0.16410335492513
WordScorePoS
land0NN
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Label: 16, with a score of: 0.130603041681223
WordScorePoS
land0NN
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However institutions often have trouble carrying out the design and enforcement of urban plans and can sometimes face resistance from citizens which can only be dealt with through open and integrated consultative processes

Label: 11, with a score of: 0.72939845998332
WordScorePoS
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Label: 16, with a score of: 0.515495216978812
WordScorePoS
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Label: 10, with a score of: 0.342600611531598
WordScorePoS
institution0.0991236547760315NN
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Institutional weaknesses such as inadequate land tenure often make it harder to implement and enforce strategic land use plans

Label: 15, with a score of: 0.579310472966277
WordScorePoS
institutional0JJ
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Label: 1, with a score of: 0.40466678775975
WordScorePoS
institutional0JJ
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Label: 11, with a score of: 0.362424288646548
WordScorePoS
institutional0JJ
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Consequently the first step in the design of sustainability policies is to review institutional capacity identify institutional obstacles and build strategy around these limitations

Label: 17, with a score of: 0.694721894298459
WordScorePoS
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Label: 1, with a score of: 0.322012609584569
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Below the roles and needs of different types of institutions are considered in urban sustainability initiatives

Label: 11, with a score of: 0.615338291417888
WordScorePoS
type0NN
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Label: 16, with a score of: 0.611442822403875
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type0NN
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Label: 10, with a score of: 0.32967917862089
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Governing the Twenty First Century City Whether in the developed or developing world cities face many similar challenges concentrations of poverty and unemployment environmental degradation lack of public safety and political corruption are only some of the most significant

Label: 11, with a score of: 0.880960077408336
WordScorePoS
century0NN
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Label: 1, with a score of: 0.224139855747373
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Label: 16, with a score of: 0.200695539009705
WordScorePoS
century0NN
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Label: 13, with a score of: 0.172252738738885
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century0.219293228426234NN
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Label: 15, with a score of: 0.153431995109255
WordScorePoS
century0NN
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In Governing the Twenty First Century City Fuchs argues that the most effective way to address these policy challenges is to strengthen urban governance and institutions

Label: 11, with a score of: 0.838052713424512
WordScorePoS
century0NN
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Label: 16, with a score of: 0.406429479532768
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Label: 13, with a score of: 0.225847560563046
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clear accountability when cities as opposed to national or state governments are the entities that have legal responsibility fiscal capacity and administrative authority to deliver public services

Label: 11, with a score of: 0.878124654845658
WordScorePoS
accountability0NN
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'Service delivery can be contracted to the private sector or to NGOs but the local government must still have the expertise and fiscal capacity to provide oversight and be accountable to civil society

Label: 16, with a score of: 0.445066399489741
WordScorePoS
contract0NN
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Label: 17, with a score of: 0.758700713232195
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'A fair governance structure with sufficient fiscal 'City governments must have the capacity and and administrative autonomy is needed to efficiently and equitably deliver public services that support an environmentally sustainable economy

Label: 17, with a score of: 0.538986149093881
WordScorePoS
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autonomy to build partnerships with businesses community groups and other government entities and provide incentives that promote entrepreneurship encourage businesses to locate in the city and drive job growth

Label: 11, with a score of: 0.567157176249005
WordScorePoS
autonomy0NN
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Label: 17, with a score of: 0.493837476768826
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autonomy0NN
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Label: 8, with a score of: 0.439209725831415
WordScorePoS
autonomy0NN
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Label: 9, with a score of: 0.340590507656928
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'A clear link between city institutions and the delivery of public services ensures the legitimacy and authority of local government thereby promoting security and public safety

Label: 11, with a score of: 0.82429015519315
WordScorePoS
city0.607697021770652NN
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Label: 16, with a score of: 0.364917112280667
WordScorePoS
city0NN
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'Strong democratic institutions of local governance and high levels of political participation enable 'The leadership of the city government is necessary for the clearly linked systematic long term planning required for infrastructure investments quality of life initiatives ease of doing business and environmental sustainability

Label: 11, with a score of: 0.58331993653951
WordScorePoS
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Label: 17, with a score of: 0.383582426768414
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Label: 16, with a score of: 0.350800499141717
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Label: 9, with a score of: 0.301817444501827
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PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS Local Government Local governments are critical to urban sustainability through their roles in development planning in the delivery of basic social services and in energy supply and management transport land use planning and waste management

Label: 11, with a score of: 0.538563549098516
WordScorePoS
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Label: 17, with a score of: 0.427686888229503
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Label: 7, with a score of: 0.406684636953459
WordScorePoS
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Label: 12, with a score of: 0.361912187474853
WordScorePoS
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Local governments can fulfill these roles only if they have sufficient governance capacity

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Bulkeley et al

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list critical factors in building governance capacity including access to financing municipal competencies the multi level governance framework and transnational networks

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Data and expertise at the local level are also necessary to provide base for integrated multi sectoral planning as seen for example in New York City PlaNYC

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Knowledge and capacity related to urban sustainability may be institutionalized in special units established in the mayoral office such as New York Office of Long term Planning and Sustainability or in special municipal agency such as Curitiba planning agency Instituto de Pesquisa Planejamento Urbano da Curitiba

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These bodies are effective because they are capable of providing high level technical and analytical inputs to urban planning they have long term continuous mandates and organizational structures they are afforded relative autonomy and there are mechanisms for public participation in the development and implementation of sustainability plans

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Pro poor adaptation to the impacts of climate change is one area where in general city and municipal governments need greater capacity

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Improved knowledge competence and accountability would increase the adaptive capacity of local bodies

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One successful example is Porto Alegre Brazil where resilient urban development is evident in environmental and social programs coupled http www

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nyc

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gov html planyc html home home

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shtml http www

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ippuc

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br default

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php with strengthened local democracy and reduced anti poor attitudes Sattherthwaite et al

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As discussed in Chapter it can be particularly challenging for cities to address climate related risks to poor and vulnerable populations

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These groups tend to live in informal settlements with insecure land tenure and little urban infrastructure and work in the informal sector with no safety nets

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Natural disasters compound risk to these communities and can foster poverty traps

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Label: 11, with a score of: 0.471564371385173
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While land tenure and informality are still issues cities grapple with globally implementing policies that build resilience in low income neighborhoods and slums help control the risk of disasters

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Enacting these policies requires coordination among departments at the municipal level and with civil society and private sector partners

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The transition toward sustainable cities will also require significant investments which means that new institutions and financial instruments will be needed

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Many possible instruments have been discussed at the international national and even local scale

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They include access to carbon finance and carbon markets Kamal Chaoui and Robert specialized climate or environmental funds socially responsible investment Labatt and White or subsidized access to capital

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In addition the worldwide trend of government decentralization has given many cities more power to raise and manage their own revenues Box

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Because of limited financial resources many municipalities in developing countries still have difficulty providing even basic services particularly to the urban poor

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From both political and budgetary standpoint this can place low priority on investments to address longer term sustainability issues

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The framework of green growth may convince city actors to see sustainability investments as an economic opportunity but access to financing will be crucial to this mainstreaming

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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD National Government Innovations at the city level require appropriate national policies

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An enabling national policy environment allows cities to experiment and be creative

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For example London was able to innovate in designing and implementing its congestion charge because the national government allowed and encouraged such experimentation

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BOX The role of cities goes beyond the cities themselves given their importance to prosperity and growth

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Urbanization increases in lock step with economic development and the ability of cities to remain attractive and efficient is crucial to secure and sustain national growth

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Thus national governments have strong economic interest in urban sustainability and they should take the lead on the coordinated design and implementation of enabling policy instruments

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National leadership helps to harmonize different goals and programs and provides scalability so that cities can exploit the most cost effective opportunities

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Public Private Partnerships To move toward sustainability cities must transform the processes of production and consumption and businesses are often at the heart of these processes Box

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Label: 12, with a score of: 0.494909288480611
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Label: 17, with a score of: 0.457531952266143
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Label: 8, with a score of: 0.162717241311909
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Moreover since the mid there has been global trend of opening the provision of public services to the private sector World Bank see Annex for examples of pilot projects

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WordScorePoS
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These services are no longer seen only as public goods but also as economic services

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Label: 5, with a score of: 0.465006472687022
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Label: 11, with a score of: 0.369428871728878
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Label: 10, with a score of: 0.333171564254098
WordScorePoS
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Decentralization of Governance In recent decades many countries have decentralized their governments to greater or lesser extent World Bank

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Label: 16, with a score of: 0.415143996875715
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Label: 2, with a score of: 0.388610080506213
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Decentralization shifts authority responsibility and accountability for public functions from the national government to local governments Republic of the Philippines

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Hence municipalities usually have the power to create and broaden their own sources of revenue in addition to receiving share of national revenues and proceeds from the utilization of natural resources within their jurisdiction

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Label: 9, with a score of: 0.339728011497203
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The most empowering forms of decentralization however provide the political space for local government action

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Label: 5, with a score of: 0.664560767708027
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Label: 1, with a score of: 0.300968141888871
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They also allow the private sector to participate in local governance particularly in the delivery of basic services and infra structure as an alternative strategy for sustainable development

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WordScorePoS
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With decentralization comes transfer of authority for planning finance and management to units of local government

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Label: 14, with a score of: 0.519337543594007
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Responsibilities for service provision rest with local governments that raise their own revenues and have independent authority to make investment decisions

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However in many developingcountry municipalities the tax base is so weak that dependence on central government subsidies still persists

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Label: 17, with a score of: 0.336202777356603
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For fiscally decentralized cities authorization of municipal borrowing is emerging as an important channel through which cities can access financing for investments that contribute to their sustainability agenda Kamal Chaoui and Robert

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The WBCSD Vision calls for new agenda for business that is compatible with good living standards and sustainable resource use

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Label: 12, with a score of: 0.381663889134069
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As part of the project global companies mapped the changes necessary to create sustainable future

Label: 12, with a score of: 0.787534162918505
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The outcome was the result of months of expert meetings and dialogues with more than companies and stakeholders in countries

Label: 12, with a score of: 0.762660904423504
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The results of this work provide strategic framework for navigating the many challenges ahead along with platform for dialogue for governments businesses and other stakeholders

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One of the conclusions is that to achieve sustainability the world will need to transform the processes of production and consumption and this will require building complex coalitions among stakeholders in order to create new sustainable solutions

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Label: 8, with a score of: 0.42084669703846
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BOX Vision The New Agenda for Business The must haves what society and corporations need to do include incorporating the costs of externalities starting with carbon ecosystem services and water halting deforestation and increasing yields from planted forests halving carbon emissions worldwide based on levels by through shift to lowcarbon energy systems and improving demand side energy efficiency and providing universal access to low carbon mobility

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Label: 15, with a score of: 0.468227569462602
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At the same time these changes will offer great opportunities

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Label: 7, with a score of: 0.324293614605023
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From the actions to develop and maintain low carbon and zero waste cities to improving biocapacity and ecosystems the business potential is estimated at trillion per year current dollars

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Label: 12, with a score of: 0.289934644409569
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Label: 15, with a score of: 0.232781180653572
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Source Adapted from http www

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wbcsd

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org vision

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Photo Shutterstock BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Telecommunications has garnered the most corporate interest accounting for percent of total investments with private sector participation from to

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Other sectors that have benefited from private investment are energy percent transport percent and water percent TABLE Examples of Private Participation in Public Services Paris World Bank

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bank0.036961556179878NN

Today many municipal services are operated by the private sector in the form of public private partnerships Table

Label: 17, with a score of: 0.784312397326457
WordScorePoS
municipal0JJ
service0NN
operate0VB
private0.0898760357768464JJ
sector0.0459744293658165NN
form0NN
public0.0390556786919513NN
partnership0.3914644359026NN

Label: 16, with a score of: 0.223070835209204
WordScorePoS
municipal0JJ
service0NN
operate0VB
private0JJ
sector0NN
form0.158049144643351NN
public0.0285977592242194NN
partnership0NN

Label: 5, with a score of: 0.43559333766979
WordScorePoS
municipal0JJ
service0.022416649027415NN
operate0VB
private0.0558185411034276JJ
sector0.0571058915334038NN
form0.100540298425703NN
public0.0727679293221751NN
partnership0NN

Even smaller services that used to be publicly controlled such as markets and bus terminals are now often transferred to private operators

Label: 17, with a score of: 0.51824060419594
WordScorePoS
service0NN
control0NN
market0.030649619577211NN
transfer0.036961556179878NN
private0.0898760357768464JJ

Label: 5, with a score of: 0.410665070885734
WordScorePoS
service0.022416649027415NN
control0.0465610847355851NN
market0NN
transfer0NN
private0.0558185411034276JJ

Label: 14, with a score of: 0.374110695243796
WordScorePoS
service0NN
control0NN
market0.0333211986002325NN
transfer0.0803666323453555NN
private0JJ

Label: 1, with a score of: 0.348073264411509
WordScorePoS
service0.0443555132287109NN
control0.0614198493690006NN
market0NN
transfer0NN
private0JJ

Label: 2, with a score of: 0.347830724224966
WordScorePoS
service0.0143446978608332NN
control0NN
market0.0913569596328122NN
transfer0NN
private0JJ

Waste Water Storage and treatment some facilities operated by the private sector Generis Paprec Nicollin REP Novergie SITA etc

Label: 6, with a score of: 0.780201223722587
WordScorePoS
waste0NN
water0.556409482163189NN
treatment0.0348974218717993NN
facility0NN
operate0VB
private0JJ
sector0.0214003646985842NN

Label: 12, with a score of: 0.505107838189669
WordScorePoS
waste0.138532104381853NN
water0.186896403054502NN
treatment0NN
facility0NN
operate0.0542519672965169VB
private0JJ
sector0.0169905820958638NN

Distribution beginning in the two service areas split between Suez and Generale des Eaux Vivendi MSW collection one part operated by the private sector Veolia Proprete Derichebourg Environnement Polyurbaine Pizzorno Dragui Wastewater Transport None identified Metro tramway and buses operated by RATP public company The construction of six new treatment plants is expected to be financed by the private sector through build operate transfer projects

Label: 6, with a score of: 0.356366507377993
WordScorePoS
service0.0084006124696649NN
collection0NN
operate0VB
private0JJ
sector0.0214003646985842NN
wastewater0.204998018174509NN
transport0NN
public0NN
company0NN
treatment0.0348974218717993NN
plant0NN
expect0VB
build0VB
transfer0NN
project0.041835863322702NN

Label: 2, with a score of: 0.2342128338439
WordScorePoS
service0.0143446978608332NN
collection0NN
operate0VB
private0JJ
sector0.0182713919265625NN
wastewater0NN
transport0NN
public0NN
company0NN
treatment0NN
plant0.132205049555723NN
expect0.0357189919948307VB
build0VB
transfer0NN
project0NN

In Acciona was awarded the contract for the largest plant

Label: 2, with a score of: 0.804281479814515
WordScorePoS
contract0NN
largest0.0714379839896614JJS
plant0.132205049555723NN

Label: 15, with a score of: 0.463002846460395
WordScorePoS
contract0NN
largest0JJS
plant0.117231723668544NN

Label: 8, with a score of: 0.317839222097352
WordScorePoS
contract0.0804764811724088NN
largest0JJS
plant0NN

Individual owner operators of small buses Pumping Eau de Paris public company with participation from Paris Glass collection Four companies under contract Polyurbaine Pizzorno Sepur Sita Mexico City Distribution contracted in to four companies that share the city

Label: 11, with a score of: 0.928246434443447
WordScorePoS
individual0JJ
paris0NN
public0.0583751976558017NN
company0NN
participation0NN
collection0NN
contract0NN
city0.607697021770652NN
share0NN

The companies are consortiums of Mexican companies Mexican banks and foreign companies Suez Generale des Eaux Severn Trent North West Water International

Label: 6, with a score of: 0.720046180431556
WordScorePoS
company0NN
bank0NN
foreign0JJ
north0RB
water0.556409482163189NN
international0.00301873916506525JJ

Label: 12, with a score of: 0.662611017311537
WordScorePoS
company0.108503934593034NN
bank0NN
foreign0JJ
north0RB
water0.186896403054502NN
international0.0023966944644375JJ

Sistema de Transporte Collectivo public company manages the metro Red de Transporte de Pasajeros operates the bus network Metrobús is jointly operated by Corredor Insurgentes SA de CV private company and Red de Transporte de Pasajeros Lagos Collection about half None identified contracted to private sector partnership operators including street sweepers Informal sector also active Billing and collection of fees contracted to private firm World Bank Development of publicprivate partnerships for the implementation of the Sustainable Sewage Sanitation Strategy Multiple private operators of mini buses LAGBUS privately operated Ongoing privatization of ferries Commuters to be expected to be privately operated PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS The advantages of private sector provision of public services compared to publicly owned utilities can include the following ' Improved management efficiency Public administration is not usually guided by profitability and can have conflicting objectives such as generating employment that reduce economic efficiency

Label: 17, with a score of: 0.77070172535114
WordScorePoS
public0.0390556786919513NN
company0NN
manage0VB
operate0VB
jointly0RB
private0.0898760357768464JJ
collection0.048933054487825NN
contract0NN
sector0.0459744293658165NN
partnership0.3914644359026NN
include0VB
active0.048933054487825JJ
world0.00418824502414673NN
bank0.036961556179878NN
development0.0469463607292177NN
implementation0.017987180284335NN
sustainable0.0115176738164035JJ
sewage0NN
sanitation0NN
strategy0.017987180284335NN
multiple0.048933054487825JJ
expect0VB
institution0.0249900578719309NN
provision0NN
service0NN
compare0VB
improve0.0183291958415182VB
management0NN
objective0.0299586785922821NN
generate0VB
employment0NN
reduce0.00335331148065841VB
economic0JJ

Label: 8, with a score of: 0.223785382525317
WordScorePoS
public0NN
company0NN
manage0VB
operate0VB
jointly0RB
private0JJ
collection0NN
contract0.0804764811724088NN
sector0.0252035516550537NN
partnership0NN
include0VB
active0JJ
world0.00172202218602071NN
bank0NN
development0.0055149369084567NN
implementation0NN
sustainable0.00688808874408283JJ
sewage0NN
sanitation0NN
strategy0.0295821503613099NN
multiple0JJ
expect0VB
institution0.0410992516792148NN
provision0NN
service0.0197870712294258NN
compare0VB
improve0.0150723186962745VB
management0NN
objective0NN
generate0VB
employment0.173804667214358NN
reduce0.0055149369084567VB
economic0.128463821595706JJ

Label: 6, with a score of: 0.174069025441854
WordScorePoS
public0NN
company0NN
manage0VB
operate0VB
jointly0RB
private0JJ
collection0NN
contract0NN
sector0.0214003646985842NN
partnership0NN
include0VB
active0JJ
world0.00438651305628668NN
bank0NN
development0NN
implementation0NN
sustainable0.00146217101876223JJ
sewage0NN
sanitation0.334686906581616NN
strategy0NN
multiple0JJ
expect0VB
institution0NN
provision0NN
service0.0084006124696649NN
compare0VB
improve0.0511916925627245VB
management0.0428007293971684NN
objective0NN
generate0VB
employment0NN
reduce0.00936547854413134VB
economic0JJ

Label: 16, with a score of: 0.166558577561667
WordScorePoS
public0.0285977592242194NN
company0NN
manage0VB
operate0VB
jointly0RB
private0JJ
collection0NN
contract0NN
sector0NN
partnership0NN
include0VB
active0JJ
world0NN
bank0NN
development0.0368309542451156NN
implementation0NN
sustainable0.00920028227269801JJ
sewage0NN
sanitation0NN
strategy0NN
multiple0JJ
expect0VB
institution0.274477239401321NN
provision0.0658099748168827NN
service0NN
compare0VB
improve0VB
management0NN
objective0NN
generate0VB
employment0NN
reduce0.0220985725470694VB
economic0JJ

The private sector is driven by financial results and is committed to reducing costs increasing bill collection and adjusting prices for improved profitability

Label: 17, with a score of: 0.53001484888165
WordScorePoS
private0.0898760357768464JJ
sector0.0459744293658165NN
drive0NN
result0NN
reduce0.00335331148065841VB
cost0NN
increase0.00432344066632653NN
collection0.048933054487825NN
price0NN
improve0.0183291958415182VB

Label: 6, with a score of: 0.400665046805747
WordScorePoS
private0JJ
sector0.0214003646985842NN
drive0NN
result0.0683326727248364NN
reduce0.00936547854413134VB
cost0NN
increase0.00905621749519576NN
collection0NN
price0NN
improve0.0511916925627245VB

In many examples of private sector provision billing rates increase significantly

Label: 5, with a score of: 0.554337743113797
WordScorePoS
private0.0558185411034276JJ
sector0.0571058915334038NN
provision0.0558185411034276NN
rate0NN
increase0.00402768349408688NN

Label: 17, with a score of: 0.449750483290713
WordScorePoS
private0.0898760357768464JJ
sector0.0459744293658165NN
provision0NN
rate0NN
increase0.00432344066632653NN

Label: 16, with a score of: 0.394665324649483
WordScorePoS
private0JJ
sector0NN
provision0.0658099748168827NN
rate0.0571955184484387NN
increase0NN

Label: 9, with a score of: 0.350944755498261
WordScorePoS
private0.0368585655748969JJ
sector0.0565629593551219NN
provision0NN
rate0NN
increase0.0159575617614215NN

However cost cutting may entail risk of lower safety and security of supply

Label: 1, with a score of: 0.513375654077536
WordScorePoS
cost0NN
cut0.120266261535276NN
risk0.0519476926891779NN
safety0NN
security0NN
supply0NN

Label: 11, with a score of: 0.406378692721037
WordScorePoS
cost0NN
cut0NN
risk0.0631828116343571NN
safety0.0731385760866977NN
security0NN
supply0NN

Label: 7, with a score of: 0.343984094275674
WordScorePoS
cost0NN
cut0NN
risk0NN
safety0NN
security0.0625160381065121NN
supply0.0528748274225028NN

Label: 2, with a score of: 0.341582169472118
WordScorePoS
cost0NN
cut0NN
risk0.0252000451111224NN
safety0NN
security0.0893850849591018NN
supply0NN

' Extended and improved service provision The entry of the private sector has also resulted in better service quality such as increased operation time and better reliability and increased access especially in developing countries

Label: 5, with a score of: 0.398291806810995
WordScorePoS
extend0VB
improve0VB
service0.022416649027415NN
provision0.0558185411034276NN
private0.0558185411034276JJ
sector0.0571058915334038NN
result0NN
quality0NN
increase0.00402768349408688NN
time0NN
access0.00780347258345989NN
develop0VB
country0NN

Label: 6, with a score of: 0.362374612490515
WordScorePoS
extend0VB
improve0.0511916925627245VB
service0.0084006124696649NN
provision0NN
private0JJ
sector0.0214003646985842NN
result0.0683326727248364NN
quality0.0255958462813622NN
increase0.00905621749519576NN
time0NN
access0.00731085509381114NN
develop0VB
country0NN

The mobile phone market took off in Uganda after CelTel entered in access reached percent in only six years

Label: 9, with a score of: 0.636302102800458
WordScorePoS
phone0.0602029956716497NN
market0.0377086395700813NN
enter0.0368585655748969VB
access0.0128821355880844NN
reach0NN

In the water and electricity sectors the trend is similar

Label: 6, with a score of: 0.913598895649805
WordScorePoS
water0.556409482163189NN
electricity0.0348974218717993NN
sector0.0214003646985842NN

Label: 12, with a score of: 0.304012917711508
WordScorePoS
water0.186896403054502NN
electricity0NN
sector0.0169905820958638NN

However privately operated service does not guarantee improved service and in some cases the service quality actually decreases

Label: 11, with a score of: 0.476953412969135
WordScorePoS
operate0VB
service0.0269743071558856NN
guarantee0NN
improve0.0273960270556257VB
quality0.0136980135278128NN
decrease0.0731385760866977NN

Label: 8, with a score of: 0.452267192611574
WordScorePoS
operate0VB
service0.0197870712294258NN
guarantee0.0804764811724088NN
improve0.0150723186962745VB
quality0.030144637392549NN
decrease0NN

' Infrastructure financing The multiple systems of urban infrastructure require high capital investment and operating budgets that local governments alone cannot provide

Label: 9, with a score of: 0.597495548862586
WordScorePoS
infrastructure0.192202909044225NN
multiple0JJ
system0NN
urban0JJ
require0.0260040237236563VB
investment0.0377086395700813NN
operate0VB
local0JJ
government0NN
provide0.00740117451847634VB

Label: 11, with a score of: 0.515708658479756
WordScorePoS
infrastructure0.0194583992186006NN
multiple0JJ
system0.0315914058171786NN
urban0.27622591898666JJ
require0VB
investment0NN
operate0VB
local0.0194583992186006JJ
government0NN
provide0.0269743071558856VB

Label: 17, with a score of: 0.343441769997594
WordScorePoS
infrastructure0.0130185595639838NN
multiple0.048933054487825JJ
system0.0211360962287062NN
urban0JJ
require0.0211360962287062VB
investment0.0766240489430275NN
operate0VB
local0.0130185595639838JJ
government0.048933054487825NN
provide0.00601568197638796VB

Private investment is needed to complement public funding of infrastructure

Label: 9, with a score of: 0.589933592858028
WordScorePoS
private0.0368585655748969JJ
investment0.0377086395700813NN
complement0NN
public0.0160169090870187NN
infrastructure0.192202909044225NN

Label: 17, with a score of: 0.660139347215822
WordScorePoS
private0.0898760357768464JJ
investment0.0766240489430275NN
complement0.0978661089756501NN
public0.0390556786919513NN
infrastructure0.0130185595639838NN

Label: 5, with a score of: 0.318850889183099
WordScorePoS
private0.0558185411034276JJ
investment0NN
complement0NN
public0.0727679293221751NN
infrastructure0.024255976440725NN

Public private partnerships such as the Chicago Infrastructure Trust Box can attract and coordinate this finance

Label: 17, with a score of: 0.889760294887854
WordScorePoS
public0.0390556786919513NN
private0.0898760357768464JJ
partnership0.3914644359026NN
infrastructure0.0130185595639838NN
attract0.048933054487825VB
coordinate0.036961556179878NN

Label: 9, with a score of: 0.352103557467319
WordScorePoS
public0.0160169090870187NN
private0.0368585655748969JJ
partnership0NN
infrastructure0.192202909044225NN
attract0VB
coordinate0NN

In regions facing rapid urbanization and very large infrastructure gaps such partnerships can play an even more critical role in financing construction to meet the increasing demand

Label: 9, with a score of: 0.632739950467543
WordScorePoS
region0NN
face0.120405991343299NN
rapid0.030745604615229JJ
urbanization0NN
infrastructure0.192202909044225NN
gap0.0602029956716497NN
partnership0NN
critical0.0454743001434393JJ
meet0VB
increase0.0159575617614215NN
demand0.030745604615229NN

Label: 17, with a score of: 0.568963732722429
WordScorePoS
region0NN
face0NN
rapid0JJ
urbanization0NN
infrastructure0.0130185595639838NN
gap0NN
partnership0.3914644359026NN
critical0.036961556179878JJ
meet0VB
increase0.00432344066632653NN
demand0NN

Label: 11, with a score of: 0.263338070462283
WordScorePoS
region0NN
face0NN
rapid0.0373517915079663JJ
urbanization0.146277152173395NN
infrastructure0.0194583992186006NN
gap0NN
partnership0NN
critical0JJ
meet0VB
increase0.00323105002784081NN
demand0NN

Label: 1, with a score of: 0.235182880082483
WordScorePoS
region0.184259548107002NN
face0NN
rapid0JJ
urbanization0NN
infrastructure0NN
gap0NN
partnership0NN
critical0JJ
meet0VB
increase0NN
demand0NN

' Innovation Competition for private participation in publics services and the prospect of profits have driven innovation in production and distribution technologies as well as in service management

Label: 9, with a score of: 0.517478404288947
WordScorePoS
innovation0.122982418460916NN
private0.0368585655748969JJ
participation0NN
public0.0160169090870187NN
service0.0148023490369527NN
drive0NN
production0.0188543197850406NN
technology0.0810848128688654NN
management0NN

Label: 17, with a score of: 0.409184224980114
WordScorePoS
innovation0.0499801157438617NN
private0.0898760357768464JJ
participation0NN
public0.0390556786919513NN
service0NN
drive0NN
production0NN
technology0.109843026967392NN
management0NN

Label: 5, with a score of: 0.315176326168426
WordScorePoS
innovation0NN
private0.0558185411034276JJ
participation0.0465610847355851NN
public0.0727679293221751NN
service0.022416649027415NN
drive0NN
production0NN
technology0.0409315617631NN
management0NN

Label: 11, with a score of: 0.308932773090185
WordScorePoS
innovation0.0373517915079663NN
private0JJ
participation0NN
public0.0583751976558017NN
service0.0269743071558856NN
drive0NN
production0NN
technology0NN
management0.0687164195125178NN

Kessides World Bank suggests that decentralized market oriented decision making freed from excessive regulation and energized by market incentives is the surest way to develop efficient innovative solutions to transportation challenges

Label: 2, with a score of: 0.535675513983608
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN
suggest0VB
market0.0913569596328122NN
orient0NN
decision0NN
free0JJ
excessive0JJ
regulation0NN
incentive0NN
develop0VB
solution0.0357189919948307NN
transportation0NN
challenge0NN

Label: 17, with a score of: 0.482504476626505
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN
suggest0VB
market0.030649619577211NN
orient0NN
decision0.0211360962287062NN
free0.0898760357768464JJ
excessive0JJ
regulation0.036961556179878NN
incentive0.036961556179878NN
develop0VB
solution0NN
transportation0NN
challenge0NN

In the water sector in South Africa Durban Metro Water partnered with Generale des Eaux and Vivendi to design and test schemes to provide free water to the poorest as mandated by law while avoiding financial burden on paying customers

Label: 6, with a score of: 0.903673472374365
WordScorePoS
water0.556409482163189NN
sector0.0214003646985842NN
south0RB
africa0IN
provide0VB
free0JJ
poorest0.0348974218717993JJS
mandate0NN
law0NN
avoid0VB
burden0NN
pay0.0295155485671997NN

Label: 12, with a score of: 0.32485316300933
WordScorePoS
water0.186896403054502NN
sector0.0169905820958638NN
south0RB
africa0IN
provide0.0200087191734851VB
free0.0332151194779746JJ
poorest0JJS
mandate0NN
law0NN
avoid0VB
burden0NN
pay0NN

Label: 16, with a score of: 0.091601443855456
WordScorePoS
water0NN
sector0NN
south0RB
africa0IN
provide0.013214597504793VB
free0JJ
poorest0JJS
mandate0NN
law0.164686343640793NN
avoid0VB
burden0NN
pay0NN

In developing countries the private sector also supplies public services that local authorities fail to deliver

Label: 17, with a score of: 0.53031382690977
WordScorePoS
develop0VB
country0NN
private0.0898760357768464JJ
sector0.0459744293658165NN
supplies0NNS
public0.0390556786919513NN
service0NN
local0.0130185595639838JJ
deliver0.0299586785922821VB

Label: 5, with a score of: 0.506523650506013
WordScorePoS
develop0VB
country0NN
private0.0558185411034276JJ
sector0.0571058915334038NN
supplies0NNS
public0.0727679293221751NN
service0.022416649027415NN
local0JJ
deliver0VB

Label: 11, with a score of: 0.433110033421052
WordScorePoS
develop0VB
country0NN
private0JJ
sector0NN
supplies0.0731385760866977NNS
public0.0583751976558017NN
service0.0269743071558856NN
local0.0194583992186006JJ
deliver0VB

Label: 9, with a score of: 0.302393897678649
WordScorePoS
develop0VB
country0NN
private0.0368585655748969JJ
sector0.0565629593551219NN
supplies0NNS
public0.0160169090870187NN
service0.0148023490369527NN
local0JJ
deliver0VB

In some peri urban areas usually poor neighborhoods small privately owned operators take the place of the public sector and operate essential services such as water supply or electricity

Label: 6, with a score of: 0.703597966434834
WordScorePoS
perus0NN
urban0JJ
poor0.0642010940957526JJ
public0NN
sector0.0214003646985842NN
operate0VB
essential0.0251182378961834JJ
service0.0084006124696649NN
water0.556409482163189NN
supply0.0590310971343993NN
electricity0.0348974218717993NN

Label: 11, with a score of: 0.460340234908692
WordScorePoS
perus0.0731385760866977NN
urban0.27622591898666JJ
poor0.0229054731708393JJ
public0.0583751976558017NN
sector0NN
operate0VB
essential0JJ
service0.0269743071558856NN
water0.0458109463416786NN
supply0NN
electricity0NN

Label: 12, with a score of: 0.363895174937645
WordScorePoS
perus0NN
urban0JJ
poor0.0339811641917276JJ
public0.028867295332595NN
sector0.0169905820958638NN
operate0.0542519672965169VB
essential0JJ
service0.00666957305782837NN
water0.186896403054502NN
supply0.0703006268489394NN
electricity0NN

Label: 1, with a score of: 0.178323083228013
WordScorePoS
perus0NN
urban0JJ
poor0.150659516460257JJ
public0NN
sector0NN
operate0VB
essential0JJ
service0.0443555132287109NN
water0NN
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For example in Cebu the Philippines percent of the population receives water from independent suppliers who pump water from private wells

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WordScorePoS
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Label: 12, with a score of: 0.311577999119329
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Those businesses may be run in an informal market when the regulatory framework is weak or inappropriate

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Where public private partnerships are used to provide services private companies will be among the key institutions for adopting efficient and low carbon systems

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In the field of adaptation to climate change as well there are good examples of collaboration between cities and the private sector

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In Barranquilla Colombia for instance the company Sociedad Acueducto extended water and sewage services to reach low income inhabitants by issuing long term local currency bond of million to refinance its debt

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In Kuala Lumpur Malaysia joint venture between the Malaysian Mining Corporation Berhad Gamuda Berhad and the government to develop dualpurpose tunnel that carries both vehicular traffic and stormwater has reduced adverse economic impacts of traffic congestion and recurring floods World Bank

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BOX Case Study The Chicago Infrastructure Trust The construction operation and renovation of infrastructure are complex and costly endeavors that require wide array of stakeholders to contribute their capital expertise and particular vision

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The budget constraints of many governments have added further dimension of difficulty in that cities can no longer depend upon infrastructure funding streams from higher levels of government

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In these circumstances even projects with high payback potential for example energy efficiency are unlikely to be realized without extensive collaboration between public and private entities

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With more actors representing various priorities and resources however it falls upon the city or metropolitan government to play the part of the maestro in the urban infrastructure orchestra

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Alongside economic and energy savings co benefits and well being improvements should be targeted

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For example tax increment financing can be used to capture returns from investments in walkable mixeduse development

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One promising example can be seen in Chicago Mayor Rahm Emmanuel recent creation of public private infrastructure bank the Chicago Infrastructure Trust that will act as the centerpiece of the city ambitious

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billion infrastructure plan

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Label: 11, with a score of: 0.335405165214695
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The plan sets out an entire sustainable development agenda and the first challenge to be tackled by the trust will be the million city building retrofit effort slated to reduce energy consumption by percent and save the city million per year

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Label: 8, with a score of: 0.104630541051568
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As with most public private partnerships the challenge for the city is to remain in control of the public realm

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Label: 17, with a score of: 0.536467800036697
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billion concession of city parking meters to private company resulted in bevy of criticism against rising rates fines and the general disorder of the meters

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More significantly the city lost its authority to manage curbside space and thus risks being unable to implement transportation projects if there is the possibility that they would negatively affect the private company parking meter revenues

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In the long run demonstration of success by the Chicago Infrastructure Trust may prove to be valuable input in the creation of more programmatic approach to sustainable transport water and energy efficiency projects that other cities can follow

Label: 11, with a score of: 0.599733191804515
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The resources and experience of given city government will be key driver in ensuring that power and information asymmetries between the public and private partners do not endanger the delivery of public benefit

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Five of the world leading infrastructure investors Citibank Citi Infrastructure Investors Macquarie J

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Morgan and Ullico have announced their intention to work with the trust

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One of the first steps for the five mayor appointed members of the trust governing board will be the establishment of clear methodology for prioritizing projects

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It is crucial that in the initial phases of the program projects be assessed on case by case basis

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Chicago proposes that each deal in its trust be structured as stand alone limited liability corporation thereby giving the particular characteristics of each potential project better chance of being taken into account and minimizing the potential risk that typically accompanies formulaic approach

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Chicago is not alone in this endeavor

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Other subnational institutions have begun to study how they can attract private dollars for public projects

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billion California Public Employees Retirement System CALPERS for example is sponsoring four infrastructure roundtables to explore how best to allocate assets in U

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infrastructure projects

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Around the world municipalities and institutional investors alike will be watching the results in Chicago

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Done right the Chicago Infrastructure Trust will not only create attractive investment opportunities but will demonstrate that sustainability focus is wise down payment towards an efficient and equitable urban future

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PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS Public private partnerships take different forms according to the level of private sector participation

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Involvement ranges across large span of functions such as capital investment production distribution of service maintenance billing and so on

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The most common types of contracts are lease agreements concessions licenses and build operate transfer arrangements

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The choice of contract should be carefully decided based on local circumstances to avoid possible bottlenecks in service provision

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In these partnerships the public authority has an essential role in framing private sector participation creating competition and enforcing good management see Box

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Effective regulation is the most critical condition for reform protecting the interests of both private investors and consumers Asian Development Bank

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WordScorePoS
regulation0.036961556179878NN
critical0.036961556179878JJ
condition0NN
reform0NN
protect0VB
private0.0898760357768464JJ
consumer0NN
development0.0469463607292177NN
bank0.036961556179878NN

An enabling policy environment that creates stability and mitigates the risks associated with investment is key to attracting private sector partners

Label: 17, with a score of: 0.63430209681891
WordScorePoS
enable0.0499801157438617VB
policy0.0641521854453136NN
environment0NN
create0VB
stability0.048933054487825NN
risk0NN
investment0.0766240489430275NN
key0NN
attract0.048933054487825VB
private0.0898760357768464JJ
sector0.0459744293658165NN

Label: 8, with a score of: 0.335193222464953
WordScorePoS
enable0VB
policy0.0452169560888236NN
environment0.0591643007226199NN
create0.0695218668857431VB
stability0NN
risk0NN
investment0.0252035516550537NN
key0NN
attract0VB
private0JJ
sector0.0252035516550537NN

Label: 9, with a score of: 0.323587044752193
WordScorePoS
enable0VB
policy0.011275328195446NN
environment0.0221298700466866NN
create0.0520080474473126VB
stability0NN
risk0NN
investment0.0377086395700813NN
key0NN
attract0VB
private0.0368585655748969JJ
sector0.0565629593551219NN

Multilateral Institutions Municipal Networks and Civil Society International agencies have long recognized their mandate to provide leadership on sustainability issues and today they increasingly understand the importance of socially inclusive competitive cities that offer good well being World Bank

Label: 11, with a score of: 0.71534536023017
WordScorePoS
multilateral0JJ
institution0NN
municipal0.0731385760866977JJ
civil0JJ
society0NN
international0JJ
agency0NN
recognize0VB
mandate0NN
provide0.0269743071558856VB
leadership0NN
sustainability0NN
issue0NN
socially0RB
inclusive0.0583751976558017JJ
city0.607697021770652NN
world0.00626002771693937NN
bank0NN

Label: 16, with a score of: 0.473241749667796
WordScorePoS
multilateral0JJ
institution0.274477239401321NN
municipal0JJ
civil0JJ
society0.118536858482514NN
international0.0189945307832458JJ
agency0NN
recognize0VB
mandate0NN
provide0.013214597504793VB
leadership0NN
sustainability0NN
issue0NN
socially0RB
inclusive0.0857932776726581JJ
city0NN
world0NN
bank0NN

Label: 17, with a score of: 0.350349256448836
WordScorePoS
multilateral0.048933054487825JJ
institution0.0249900578719309NN
municipal0JJ
civil0.0978661089756501JJ
society0.0359743605686701NN
international0.00648516099948979JJ
agency0NN
recognize0VB
mandate0NN
provide0.00601568197638796VB
leadership0.036961556179878NN
sustainability0.036961556179878NN
issue0.0299586785922821NN
socially0RB
inclusive0.0130185595639838JJ
city0NN
world0.00418824502414673NN
bank0.036961556179878NN

Multilateral institutions are engaging with cities and providing tools and knowledge to deliver improved value for public spending while promoting sustainable development

Label: 11, with a score of: 0.736127843914129
WordScorePoS
multilateral0JJ
institution0NN
engage0VB
city0.607697021770652NN
provide0.0269743071558856VB
tool0NN
knowledge0NN
deliver0VB
improve0.0273960270556257VB
public0.0583751976558017NN
spending0NN
promote0VB
sustainable0.00782503464617421JJ
development0.0100241617629471NN

Label: 16, with a score of: 0.383292685970091
WordScorePoS
multilateral0JJ
institution0.274477239401321NN
engage0VB
city0NN
provide0.013214597504793VB
tool0NN
knowledge0NN
deliver0VB
improve0VB
public0.0285977592242194NN
spending0NN
promote0.0220985725470694VB
sustainable0.00920028227269801JJ
development0.0368309542451156NN

A good example of such program is the European Union Reference Framework for Sustainable Cities RFSC

Label: 11, with a score of: 0.987087116697266
WordScorePoS
framework0.0194583992186006NN
sustainable0.00782503464617421JJ
city0.607697021770652NN

This is an online operational toolkit to assist local authorities and other bodies in improving the design and promotion of sustainable development strategies and projects

Label: 17, with a score of: 0.601133261519579
WordScorePoS
online0.048933054487825NN
assist0.048933054487825VB
local0.0130185595639838JJ
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promotion0.0249900578719309NN
sustainable0.0115176738164035JJ
development0.0469463607292177NN
strategy0.017987180284335NN
project0NN

eumayors

Label: 0, with a score of: 1

eu news en

Label: 0, with a score of: 1

id news Community based organizations and the private sector can also use the RFSC as resource when participating in urban sustainability planning and programming

Label: 11, with a score of: 0.627565871480823
WordScorePoS
community0NN
base0NN
organization0NN
private0JJ
sector0NN
resource0.0449504361447405NN
urban0.27622591898666JJ
sustainability0NN
plan0.0583751976558017NN

Label: 17, with a score of: 0.469051820759967
WordScorePoS
community0NN
base0.017987180284335NN
organization0.0422721924574123NN
private0.0898760357768464JJ
sector0.0459744293658165NN
resource0.0375923735942499NN
urban0JJ
sustainability0.036961556179878NN
plan0.0130185595639838NN

The RFSC provides multipurpose multi stakeholder decision making and communication tool with broad range of questions that can help politicians city managers planners citizens businesses and civil society organizations to review their approaches toward sustainability

Label: 11, with a score of: 0.67430954523194
WordScorePoS
multus0NN
stakeholder0NN
decision0NN
communication0NN
tool0NN
range0NN
city0.607697021770652NN
business0NN
civil0JJ
society0NN
organization0NN
review0NN
approach0NN
sustainability0NN

Label: 17, with a score of: 0.546974807612478
WordScorePoS
multus0.0978661089756501NN
stakeholder0.0739231123597559NN
decision0.0211360962287062NN
communication0.0499801157438617NN
tool0NN
range0NN
city0NN
business0NN
civil0.0978661089756501JJ
society0.0359743605686701NN
organization0.0422721924574123NN
review0.036961556179878NN
approach0NN
sustainability0.036961556179878NN

Tools are also available to monitor implementation and to evaluate results

Label: 12, with a score of: 0.611359770996485
WordScorePoS
tool0.0542519672965169NN
monitor0.0332151194779746NN
implementation0.0199423462679015NN
result0NN

Label: 10, with a score of: 0.496188070064145
WordScorePoS
tool0NN
monitor0.0396106020715138NN
implementation0.0475643836183697NN
result0NN

Label: 17, with a score of: 0.44342194942762
WordScorePoS
tool0NN
monitor0.0599173571845643NN
implementation0.017987180284335NN
result0NN

Label: 6, with a score of: 0.388940207254845
WordScorePoS
tool0NN
monitor0NN
implementation0NN
result0.0683326727248364NN

The toolkit is open and flexible and can be adapted to suit various political geographic economic environmental and social contexts

Label: 8, with a score of: 0.379006631834874
WordScorePoS
political0JJ
geographic0JJ
economic0.128463821595706JJ
environmental0.0295821503613099JJ
social0.0504071033101074JJ
context0NN

Label: 10, with a score of: 0.559257190815151
WordScorePoS
political0.0792212041430275JJ
geographic0JJ
economic0.103276847978089JJ
environmental0.0237821918091848JJ
social0.101310366625194JJ
context0NN

Label: 1, with a score of: 0.460352071678888
WordScorePoS
political0JJ
geographic0JJ
economic0.0959899298575892JJ
environmental0.0442083771593746JJ
social0.112994637345193JJ
context0NN

Label: 5, with a score of: 0.387197348917308
WordScorePoS
political0.111637082206855JJ
geographic0JJ
economic0.0727679293221751JJ
environmental0JJ
social0.0285529457667019JJ
context0NN

Also notable among the institutions involved in action toward sustainable cities are transnational municipal networks such as ICLEI Metropolis and UCLG

Label: 11, with a score of: 0.873271198890123
WordScorePoS
institution0NN
involve0VB
action0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN
transnational0JJ
municipal0.0731385760866977JJ

Label: 16, with a score of: 0.35972349479289
WordScorePoS
institution0.274477239401321NN
involve0VB
action0NN
sustainable0.00920028227269801JJ
city0NN
transnational0JJ
municipal0JJ

These networks are helping cities become more involved in the global climate change agenda and more broadly in the sustainability agenda

Label: 13, with a score of: 0.669857628663291
WordScorePoS
help0NN
city0NN
involve0VB
global0.0375696197697663JJ
climate0.291881734282273NN
change0.402565546215053NN
agenda0NN
sustainability0NN

Label: 11, with a score of: 0.610550134195477
WordScorePoS
help0NN
city0.607697021770652NN
involve0VB
global0.00501208088147356JJ
climate0.0229054731708393NN
change0.0315914058171786NN
agenda0NN
sustainability0NN

Networks allow for the exchange of best practices peer to peer learning and collaborative development of innovative approaches toward sustainable cities

Label: 11, with a score of: 0.867678965956612
WordScorePoS
practice0NN
learn0VB
development0.0100241617629471NN
approach0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN

Label: 4, with a score of: 0.390002145272941
WordScorePoS
practice0NN
learn0.244520809605025VB
development0.0279277626235394NN
approach0NN
sustainable0.00872035848132902JJ
city0NN

For example in the absence of global climate agreement at the national level municipal networks often allow cities to enter into shadow agreements such as the Urban Environmental Accords described in Box and potentially even city level emissions trading schemes

Label: 11, with a score of: 0.897862433307212
WordScorePoS
global0.00501208088147356JJ
climate0.0229054731708393NN
agreement0NN
national0.00501208088147356JJ
level0.00692515656684925NN
municipal0.0731385760866977JJ
city0.607697021770652NN
enter0VB
urban0.27622591898666JJ
environmental0.0537696560159885JJ
potentially0RB
emission0.0373517915079663NN
trading0NN

Label: 13, with a score of: 0.351794966248977
WordScorePoS
global0.0375696197697663JJ
climate0.291881734282273NN
agreement0.0236803262479443NN
national0.0112708859309299JJ
level0.0415277415054218NN
municipal0JJ
city0NN
enter0VB
urban0JJ
environmental0JJ
potentially0RB
emission0.19598742448524NN
trading0NN

At the national and local levels the rise of civil society as an institutional sector should not be underestimated

Label: 13, with a score of: 0.549352762747326
WordScorePoS
national0.0112708859309299JJ
local0.0145856516935579JJ
level0.0415277415054218NN
rise0.120914181920259NN
civil0JJ
society0NN
institutional0.0828215106044499JJ
sector0NN

Label: 17, with a score of: 0.520961217183309
WordScorePoS
national0.0134132459226336JJ
local0.0130185595639838JJ
level0.0138997399903163NN
rise0NN
civil0.0978661089756501JJ
society0.0359743605686701NN
institutional0.036961556179878JJ
sector0.0459744293658165NN

Label: 16, with a score of: 0.423621909036902
WordScorePoS
national0.0294647633960925JJ
local0JJ
level0.0610668815653026NN
rise0NN
civil0JJ
society0.118536858482514NN
institutional0JJ
sector0NN

It has provided mechanism for individual citizens and communities to collaborate with government on the delivery of public goods and services and advocate for accountability

Label: 17, with a score of: 0.791211554037962
WordScorePoS
mechanism0.147846224719512NN
individual0JJ
community0NN
government0.048933054487825NN
public0.0390556786919513NN
service0NN
accountability0.048933054487825NN

Label: 10, with a score of: 0.305980710489249
WordScorePoS
mechanism0NN
individual0.064698038783335JJ
community0.0202620733250388NN
government0NN
public0.0172128079963482NN
service0.00795378154685591NN
accountability0NN

NGOs civil society organizations and community organizations can strengthen service provision and environmental management improve the livelihoods of communities and even contribute to urban planning Box

Label: 11, with a score of: 0.595280422518325
WordScorePoS
civil0JJ
society0NN
organization0NN
community0NN
strengthen0.0179828714372571VB
service0.0269743071558856NN
provision0NN
environmental0.0537696560159885JJ
management0.0687164195125178NN
improve0.0273960270556257VB
livelihood0NN
contribute0VB
urban0.27622591898666JJ
plan0.0583751976558017NN

Label: 17, with a score of: 0.322414122242691
WordScorePoS
civil0.0978661089756501JJ
society0.0359743605686701NN
organization0.0422721924574123NN
community0NN
strengthen0.0120313639527759VB
service0NN
provision0NN
environmental0JJ
management0NN
improve0.0183291958415182VB
livelihood0NN
contribute0.0249900578719309VB
urban0JJ
plan0.0130185595639838NN

In many cities civil society and community based BUILDING SUSTAINABILITY IN AN URBANIZING WORLD disaster risk management have proven to be more successful than local government interventions for building resilience to climate change

Label: 11, with a score of: 0.689186271776515
WordScorePoS
city0.607697021770652NN
civil0JJ
society0NN
community0NN
base0NN
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
disaster0.157957029085893NN
risk0.0631828116343571NN
management0.0687164195125178NN
successful0JJ
local0.0194583992186006JJ
government0NN
intervention0NN
resilience0.0373517915079663NN
climate0.0229054731708393NN
change0.0315914058171786NN

Label: 13, with a score of: 0.482767737980098
WordScorePoS
city0NN
civil0JJ
society0NN
community0.0343390275626203NN
base0NN
building0NN
sustainability0NN
world0.00351929966767313NN
disaster0.0236803262479443NN
risk0NN
management0.0171695137813101NN
successful0JJ
local0.0145856516935579JJ
government0NN
intervention0NN
resilience0.0279982034978914NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 17, with a score of: 0.357304780583944
WordScorePoS
city0NN
civil0.0978661089756501JJ
society0.0359743605686701NN
community0NN
base0.017987180284335NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
disaster0NN
risk0NN
management0NN
successful0.048933054487825JJ
local0.0130185595639838JJ
government0.048933054487825NN
intervention0NN
resilience0NN
climate0NN
change0NN

A strong evidence base is key here as well informed civil society will be better able to prepare and respond to extreme weather events

Label: 1, with a score of: 0.742951883022819
WordScorePoS
strong0JJ
evidence0NN
base0.0442083771593746NN
key0NN
civil0JJ
society0NN
extreme0.294526332970048JJ
weather0NN
event0.0908430554521382NN

Label: 10, with a score of: 0.34944049303868
WordScorePoS
strong0.0488696285210061JJ
evidence0.12939607756667NN
base0NN
key0NN
civil0JJ
society0.0237821918091848NN
extreme0JJ
weather0NN
event0NN

Civil society operates in the space where the private and public sectors fail to deliver services equitably

Label: 17, with a score of: 0.672217549259902
WordScorePoS
civil0.0978661089756501JJ
society0.0359743605686701NN
operate0VB
space0.0299586785922821NN
private0.0898760357768464JJ
public0.0390556786919513NN
sector0.0459744293658165NN
deliver0.0299586785922821VB
service0NN

Label: 5, with a score of: 0.440571523075049
WordScorePoS
civil0JJ
society0.0335134328085675NN
operate0VB
space0NN
private0.0558185411034276JJ
public0.0727679293221751NN
sector0.0571058915334038NN
deliver0VB
service0.022416649027415NN

Urban sustainability will rely on governments and businesses empowering training and partnering with the civil society organizations that are filling these gaps

Label: 11, with a score of: 0.504000164028503
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
rely0VB
government0NN
business0NN
empower0VB
training0NN
civil0JJ
society0NN
organization0NN
gap0NN

Label: 17, with a score of: 0.478056906775766
WordScorePoS
urban0JJ
sustainability0.036961556179878NN
rely0VB
government0.048933054487825NN
business0NN
empower0VB
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civil0.0978661089756501JJ
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organization0.0422721924574123NN
gap0NN

Label: 9, with a score of: 0.439518494860815
WordScorePoS
urban0JJ
sustainability0NN
rely0VB
government0NN
business0.0909486002868787NN
empower0.0368585655748969VB
training0.030745604615229NN
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gap0.0602029956716497NN

Participation in Urban Governance BOX There is now consensus that the quality of governance depends on participation and accountability ASEAN Studies Center

Label: 11, with a score of: 0.558873117438048
WordScorePoS
participation0NN
urban0.27622591898666JJ
governance0NN
consensus0NN
quality0.0136980135278128NN
depend0VB
accountability0NN
study0NN

Label: 16, with a score of: 0.62604919762331
WordScorePoS
participation0.0548954478802642NN
urban0JJ
governance0.107490825192354NN
consensus0NN
quality0NN
depend0VB
accountability0NN
study0NN

While the participation of citizens alone does not necessarily guarantee better governance or service provision it helps to link leaders with stakeholders fostering shared vision and understanding of the necessary tools to achieve it

Label: 17, with a score of: 0.495162598056076
WordScorePoS
participation0NN
guarantee0NN
governance0NN
service0NN
provision0NN
help0NN
stakeholder0.0739231123597559NN
foster0.036961556179878JJ
share0.0766240489430275NN
vision0.048933054487825NN
tool0NN
achieve0.00463324666343875VB

Label: 16, with a score of: 0.468709896246847
WordScorePoS
participation0.0548954478802642NN
guarantee0NN
governance0.107490825192354NN
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provision0.0658099748168827NN
help0NN
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foster0JJ
share0NN
vision0NN
tool0NN
achieve0VB

Label: 5, with a score of: 0.368169253082012
WordScorePoS
participation0.0465610847355851NN
guarantee0NN
governance0NN
service0.022416649027415NN
provision0.0558185411034276NN
help0NN
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foster0JJ
share0.0285529457667019NN
vision0NN
tool0NN
achieve0.0258977780207001VB

Label: 12, with a score of: 0.352387136006162
WordScorePoS
participation0NN
guarantee0NN
governance0NN
service0.00666957305782837NN
provision0NN
help0.0542519672965169NN
stakeholder0.0409791940864438NN
foster0JJ
share0NN
vision0NN
tool0.0542519672965169NN
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History and local conditions influence the mechanisms and impact of participatory processes but in all cases governance models are changing from traditionaltechnocratic control to participatory approaches that rely increasingly on civil society and the business community UN Habitat

Label: 13, with a score of: 0.59581079951812
WordScorePoS
history0.0414107553022249NN
local0.0145856516935579JJ
condition0NN
mechanism0.0414107553022249NN
impact0.0437569550806737NN
participatory0JJ
process0NN
governance0NN
change0.402565546215053NN
control0NN
approach0NN
rely0VB
civil0JJ
society0NN
business0NN
community0.0343390275626203NN
habitat0NN

Label: 17, with a score of: 0.303750359761049
WordScorePoS
history0NN
local0.0130185595639838JJ
condition0NN
mechanism0.147846224719512NN
impact0NN
participatory0JJ
process0NN
governance0NN
change0NN
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rely0VB
civil0.0978661089756501JJ
society0.0359743605686701NN
business0NN
community0NN
habitat0NN

Label: 16, with a score of: 0.429810656938558
WordScorePoS
history0NN
local0JJ
condition0NN
mechanism0NN
impact0.0285977592242194NN
participatory0.0811931365363091JJ
process0NN
governance0.107490825192354NN
change0NN
control0NN
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rely0VB
civil0JJ
society0.118536858482514NN
business0NN
community0NN
habitat0NN

The process can be as simple as holding elections consulting with grassroots organizations or mobilizing their assistance

Label: 5, with a score of: 0.642490567665005
WordScorePoS
process0.0558185411034276NN
simple0JJ
hold0.18234260265061NN
organization0NN
mobilize0VB
assistance0NN

Label: 17, with a score of: 0.623337487911851
WordScorePoS
process0NN
simple0.048933054487825JJ
hold0NN
organization0.0422721924574123NN
mobilize0.0898760357768464VB
assistance0.0499801157438617NN

In the electoral sphere deeper modality of participation includes such tools as the ballot initiative referenda and recall elections

Label: 0, with a score of: 1

These are widespread in North America Reviving the Urban Environmental Accords In June in San Francisco city mayors from around the world gathered and signed the Urban Environmental Accords UEA recognizing that cities are the main culprit of environmental degradation and have the responsibility and authority to solve consequent problems

Label: 11, with a score of: 0.968984011237773
WordScorePoS
north0RB
urban0.27622591898666JJ
environmental0.0537696560159885JJ
city0.607697021770652NN
world0.00626002771693937NN
recognize0VB
degradation0NN
responsibility0NN

Label: 15, with a score of: 0.111557780161663
WordScorePoS
north0RB
urban0JJ
environmental0JJ
city0NN
world0.00110699520004297NN
recognize0VB
degradation0.221714625071441NN
responsibility0NN

They agreed to implement activities in environment related sectors for example energy and waste management and to evaluate cities efforts and performance in

Label: 11, with a score of: 0.700305948380109
WordScorePoS
agree0VB
implement0.0273960270556257VB
activity0NN
environment0.0268848280079943NN
related0.0112376090361851JJ
sector0NN
energy0.0492535935240667NN
waste0.0373517915079663NN
management0.0687164195125178NN
city0.607697021770652NN
effort0.0229054731708393NN
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Label: 7, with a score of: 0.458238312671208
WordScorePoS
agree0VB
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activity0NN
environment0NN
related0JJ
sector0NN
energy0.494617163780726NN
waste0.0625160381065121NN
management0NN
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effort0NN
performance0NN

Label: 12, with a score of: 0.390904213636033
WordScorePoS
agree0.0234335422829798VB
implement0.0203215381458133VB
activity0.0199423462679015NN
environment0.079769385071606NN
related0.00833571598658786JJ
sector0.0169905820958638NN
energy0.133960988253756NN
waste0.138532104381853NN
management0.0339811641917276NN
city0NN
effort0NN
performance0NN

While the UEA has signatory cities worldwide no governing council had been held since its founding in nor has it been acknowledged as UN affiliated organization

Label: 11, with a score of: 0.943313821128545
WordScorePoS
city0.607697021770652NN
worldwide0RB
hold0NN
acknowledge0VB
organization0NN

Label: 5, with a score of: 0.283046141578405
WordScorePoS
city0NN
worldwide0RB
hold0.18234260265061NN
acknowledge0VB
organization0NN

To address this the City of Gwangju Korea organized the UEA Gwangju Summit

Label: 11, with a score of: 0.982213050224981
WordScorePoS
city0.607697021770652NN
organize0VB

Gwangju co hosted the Summit with UNEP and the City and County of San Francisco

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

The UEA Gwangju Summit was attended by total of mayors and representatives from cities worldwide as well as experts from international organizations

Label: 11, with a score of: 0.941295986642427
WordScorePoS
attend0VB
total0NN
representative0NN
city0.607697021770652NN
worldwide0RB
international0JJ
organization0NN

The meeting was important in gathering city representatives and introducing the idea of an Urban Clean Development Mechanism CDM

Label: 11, with a score of: 0.95212136337549
WordScorePoS
city0.607697021770652NN
representative0NN
introduce0VB
idea0.0731385760866977NN
urban0.27622591898666JJ
clean0JJ
development0.0100241617629471NN
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Label: 17, with a score of: 0.191778439217543
WordScorePoS
city0NN
representative0NN
introduce0VB
idea0NN
urban0JJ
clean0JJ
development0.0469463607292177NN
mechanism0.147846224719512NN

Label: 7, with a score of: 0.123097377515809
WordScorePoS
city0NN
representative0NN
introduce0VB
idea0NN
urban0JJ
clean0.125032076213024JJ
development0NN
mechanism0NN

Major outcomes included the adoption of the Gwangju Declaration which advocates for an Urban Environment Evaluation Index and an Urban CDM the establishment of the Global Low Carbon Green City Award in partnership with UNEP the agreement to hold UEA Summit every other year and the establishment of the UEA Secretariat in Gwangju

Label: 11, with a score of: 0.900143795743849
WordScorePoS
major0JJ
outcome0NN
include0VB
adoption0NN
declaration0NN
urban0.27622591898666JJ
environment0.0268848280079943NN
global0.00501208088147356JJ
carbon0.0373517915079663NN
green0.04477822029736JJ
city0.607697021770652NN
partnership0NN
agreement0NN
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Label: 17, with a score of: 0.290764480118733
WordScorePoS
major0JJ
outcome0NN
include0VB
adoption0NN
declaration0NN
urban0JJ
environment0NN
global0.0201198688839505JJ
carbon0NN
green0JJ
city0NN
partnership0.3914644359026NN
agreement0NN
hold0NN

Label: 5, with a score of: 0.161709425957399
WordScorePoS
major0JJ
outcome0.0465610847355851NN
include0VB
adoption0NN
declaration0NN
urban0JJ
environment0NN
global0JJ
carbon0NN
green0JJ
city0NN
partnership0NN
agreement0NN
hold0.18234260265061NN

Additionally Urban CDM pilot testing will be conducted in qualified UEA signatory cities

Label: 11, with a score of: 0.99063665162973
WordScorePoS
additionally0RB
urban0.27622591898666JJ
conduct0NN
city0.607697021770652NN

The amendment of the Indian constitution th Constitutional Amendment Act provided for local government devolution and vested the functions of urban economic and social development planning with local urban bodies

Label: 11, with a score of: 0.797640418040233
WordScorePoS
local0.0194583992186006JJ
government0NN
function0NN
urban0.27622591898666JJ
economic0.0389167984372011JJ
social0.0458109463416786JJ
development0.0100241617629471NN
plan0.0583751976558017NN

Label: 8, with a score of: 0.243425965791403
WordScorePoS
local0.0214106369326177JJ
government0NN
function0NN
urban0JJ
economic0.128463821595706JJ
social0.0504071033101074JJ
development0.0055149369084567NN
plan0NN

Label: 10, with a score of: 0.379791227769385
WordScorePoS
local0JJ
government0NN
function0NN
urban0.0488696285210061JJ
economic0.103276847978089JJ
social0.101310366625194JJ
development0.0177346522508396NN
plan0.0344256159926965NN

However full devolution has been slow to take place due to lack of corresponding fiscal devolution an institutional framework for planning and professional staff capacity

Label: 13, with a score of: 0.521106018930045
WordScorePoS
full0JJ
slow0JJ
due0.0473606524958885JJ
lack0NN
institutional0.0828215106044499JJ
framework0.0291713033871158NN
plan0.0291713033871158NN
capacity0.025270503924109NN

Label: 8, with a score of: 0.444283189756929
WordScorePoS
full0.0410992516792148JJ
slow0.0607878664258118JJ
due0JJ
lack0.0252035516550537NN
institutional0JJ
framework0.0428212738652355NN
plan0NN
capacity0.0123650647908625NN

Label: 1, with a score of: 0.388215734030215
WordScorePoS
full0JJ
slow0JJ
due0.0519476926891779JJ
lack0.0753297582301286NN
institutional0JJ
framework0.0319966432858631NN
plan0NN
capacity0NN

As result most urban plans are still developed by the Town and Country Planning Organization national government agency

Label: 11, with a score of: 0.832722455456395
WordScorePoS
result0NN
urban0.27622591898666JJ
plan0.0583751976558017NN
develop0VB
country0NN
organization0NN
national0.00501208088147356JJ
government0NN
agency0NN

Label: 10, with a score of: 0.323335460841005
WordScorePoS
result0NN
urban0.0488696285210061JJ
plan0.0344256159926965NN
develop0VB
country0NN
organization0.02794560829783NN
national0.00886732612541978JJ
government0NN
agency0NN

Furthermore there has been limited participation in the planning processes by communities and civil society organizations

Label: 17, with a score of: 0.459683059818486
WordScorePoS
limited0JJ
participation0NN
plan0.0130185595639838NN
process0NN
community0NN
civil0.0978661089756501JJ
society0.0359743605686701NN
organization0.0422721924574123NN

Label: 1, with a score of: 0.441587733086042
WordScorePoS
limited0.120266261535276JJ
participation0.0614198493690006NN
plan0NN
process0NN
community0NN
civil0JJ
society0NN
organization0NN

Label: 16, with a score of: 0.42152687747812
WordScorePoS
limited0JJ
participation0.0548954478802642NN
plan0NN
process0NN
community0NN
civil0JJ
society0.118536858482514NN
organization0NN

Label: 5, with a score of: 0.330287810177906
WordScorePoS
limited0JJ
participation0.0465610847355851NN
plan0NN
process0.0558185411034276NN
community0NN
civil0JJ
society0.0335134328085675NN
organization0NN

Urban planning has remained primarily technical expert driven process unrelated to the capacity of the local government for implementation and for which communities had no sense of ownership

Label: 11, with a score of: 0.644084234343844
WordScorePoS
urban0.27622591898666JJ
plan0.0583751976558017NN
remain0VB
primarily0RB
technical0.0373517915079663JJ
drive0NN
process0NN
capacity0.0112376090361851NN
local0.0194583992186006JJ
government0NN
implementation0NN
community0NN
sense0NN
ownership0NN

Label: 10, with a score of: 0.408590833734216
WordScorePoS
urban0.0488696285210061JJ
plan0.0344256159926965NN
remain0.0396106020715138VB
primarily0RB
technical0JJ
drive0NN
process0NN
capacity0NN
local0JJ
government0NN
implementation0.0475643836183697NN
community0.0202620733250388NN
sense0.064698038783335NN
ownership0NN

In recognition of this situation the Society for Participatory Research in Asia PRIA an Indian NGO undertook to support participatory urban planning in the state of Chhattisgarh focusing particularly on addressing the needs of informal communities whose settlements are typically not reflected in city plans

Label: 11, with a score of: 0.933848547531149
WordScorePoS
situation0.0631828116343571NN
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participatory0.055245183797332JJ
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asia0IN
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support0.0224752180723703NN
urban0.27622591898666JJ
plan0.0583751976558017NN
focus0.0373517915079663NN
address0NN
community0NN
settlement0.146277152173395NN
typically0RB
city0.607697021770652NN

PRIA and its partners established urban resource centers in two towns Rajnandgaon and Janjgir to advocate for the needs of poor groups as well as engage with civil servants and politicians to support pro poor accountable governance

Label: 1, with a score of: 0.523509829755383
WordScorePoS
establish0VB
urban0JJ
resource0.0739147683583536NN
poor0.150659516460257JJ
engage0VB
civil0JJ
support0.0184786920895884NN
accountable0JJ
governance0NN

Label: 11, with a score of: 0.517858743975728
WordScorePoS
establish0VB
urban0.27622591898666JJ
resource0.0449504361447405NN
poor0.0229054731708393JJ
engage0VB
civil0JJ
support0.0224752180723703NN
accountable0JJ
governance0NN

Label: 16, with a score of: 0.358848947257948
WordScorePoS
establish0VB
urban0JJ
resource0NN
poor0JJ
engage0VB
civil0JJ
support0NN
accountable0.162386273072618JJ
governance0.107490825192354NN

Label: 17, with a score of: 0.345917729599229
WordScorePoS
establish0.048933054487825VB
urban0JJ
resource0.0375923735942499NN
poor0.0153248097886055JJ
engage0VB
civil0.0978661089756501JJ
support0.0451108483130998NN
accountable0JJ
governance0NN

In the actual urban planning process PRIA conducted several rounds of collaborative multi stakeholder consultation with the local government during which vision statements were developed for each city set of projects was identified and phased for implementation the projects were integrated into the municipal budget in order to ensure financial viability without dependence on external resources area specific urban design guidelines were prepared for informal settlements cultural heritage areas and so on

Label: 11, with a score of: 0.909439281754273
WordScorePoS
actual0JJ
urban0.27622591898666JJ
plan0.0583751976558017NN
process0NN
conduct0NN
round0NN
multus0NN
stakeholder0NN
local0.0194583992186006JJ
government0NN
vision0NN
develop0VB
city0.607697021770652NN
set0NN
project0NN
implementation0NN
integrate0.0537696560159885VB
municipal0.0731385760866977JJ
ensure0.00501208088147356VB
external0JJ
resource0.0449504361447405NN
guideline0NN
settlement0.146277152173395NN
cultural0.055245183797332JJ
heritage0.0731385760866977NN

process was BOX Case Study Participatory City Planning in Chhattisgarh created to review and modify the plan annually and the informal sector was actively engaged in the planning process

Label: 11, with a score of: 0.862188149275898
WordScorePoS
process0NN
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participatory0.055245183797332JJ
city0.607697021770652NN
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review0NN
annually0RB
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engage0VB

Several challenges were encountered during this process including the need to deal with urban departments with overlapping functions the lack of legislation to enable community and civil society collaboration the perception among local leaders and municipal officials that participatory planning indicates their failure and is challenge to their authority and unrealistic community expectations

Label: 11, with a score of: 0.766840294082762
WordScorePoS
challenge0.0947742174515357NN
process0NN
include0VB
urban0.27622591898666JJ
function0NN
lack0.0229054731708393NN
legislation0NN
enable0.0373517915079663VB
community0NN
civil0JJ
society0NN
local0.0194583992186006JJ
municipal0.0731385760866977JJ
participatory0.055245183797332JJ
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The partnership approach that was initiated and developed by PRIA generated demand based plans for the towns of Rajnandgaon and Janjgir while at the same time ensuring feasibility of the projects by identifying and prioritizing the necessary resources

Label: 17, with a score of: 0.713662446295304
WordScorePoS
partnership0.3914644359026NN
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develop0VB
generate0VB
demand0NN
base0.017987180284335NN
plan0.0130185595639838NN
time0NN
ensure0.00335331148065841VB
project0NN
identify0VB
resource0.0375923735942499NN

Label: 14, with a score of: 0.369720282264808
WordScorePoS
partnership0NN
approach0NN
develop0VB
generate0.0401833161726777VB
demand0NN
base0.058665107245718NN
plan0.0141533244035107NN
time0.0166605993001162NN
ensure0VB
project0NN
identify0.0531983120572613VB
resource0.0572167664438909NN

Label: 12, with a score of: 0.340591953512871
WordScorePoS
partnership0NN
approach0.0542519672965169NN
develop0VB
generate0VB
demand0NN
base0.0199423462679015NN
plan0.0144336476662975NN
time0.0169905820958638NN
ensure0.00743561777158133VB
project0.0664302389559493NN
identify0VB
resource0.0416785799329393NN

Realistic and implementable plans arose because projects were prioritized and then the urban plans were developed around these projects as opposed to plans being the driver of project selection

Label: 11, with a score of: 0.759835029183378
WordScorePoS
plan0.0583751976558017NN
arise0VB
project0NN
urban0.27622591898666JJ
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In contrast to the alternative of state led urban planning the participatory process also built local government capacity and provided clear link between urban planners and those that would be responsible for implementation

Label: 11, with a score of: 0.845116197740981
WordScorePoS
alternative0NN
lead0NN
urban0.27622591898666JJ
plan0.0583751976558017NN
participatory0.055245183797332JJ
process0NN
build0VB
local0.0194583992186006JJ
government0NN
capacity0.0112376090361851NN
responsible0JJ
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Notably capacity building was also undertaken to assist local bodies to identify and raise internal revenue to fund the planned projects

Label: 17, with a score of: 0.658431188528528
WordScorePoS
notably0RB
capacity0.0451108483130998NN
building0.149940347231585NN
undertake0VB
assist0.048933054487825VB
local0.0130185595639838JJ
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revenue0.0978661089756501NN
fund0NN
plan0.0130185595639838NN
project0NN

Label: 13, with a score of: 0.451993255960044
WordScorePoS
notably0RB
capacity0.025270503924109NN
building0NN
undertake0.0414107553022249VB
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local0.0145856516935579JJ
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raise0.0671298309154199NN
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fund0.0414107553022249NN
plan0.0291713033871158NN
project0.0335649154577099NN

Label: 11, with a score of: 0.391993719608718
WordScorePoS
notably0RB
capacity0.0112376090361851NN
building0.0747035830159326NN
undertake0VB
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local0.0194583992186006JJ
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fund0.055245183797332NN
plan0.0583751976558017NN
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The final product of the participatory planning process was phased implementation strategy which included an investment strategy the role of each partner in the short medium and long term and the project phasing strategy

Label: 17, with a score of: 0.522839813988463
WordScorePoS
product0.017987180284335NN
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plan0.0130185595639838NN
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implementation0.017987180284335NN
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investment0.0766240489430275NN
medium0NN
term0.149793392961411NN
project0NN
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Label: 12, with a score of: 0.363911859494582
WordScorePoS
product0.0199423462679015NN
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implementation0.0199423462679015NN
strategy0NN
include0VB
investment0NN
medium0NN
term0NN
project0.0664302389559493NN
phase0.108503934593034NN

Label: 9, with a score of: 0.319317941875701
WordScorePoS
product0.0442597400933733NN
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process0.0737171311497939NN
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investment0.0377086395700813NN
medium0.0454743001434393NN
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project0NN
phase0NN

Source Sheikh and Rao

Label: 6, with a score of: 0.73415387760794
WordScorePoS
source0.0613562156822703NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD BOX and Japan and are spreading into Europe and Latin America

Label: 17, with a score of: 0.775361318902682
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
spread0NN

Label: 16, with a score of: 0.445484052617579
WordScorePoS
building0.109790895760528NN
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Participatory budgeting is probably one of the most effective ways to give citizens say in how tax revenues are allocated and experience with this process over more than two decades in Brazil and elsewhere has produced important practical lessons

Label: 17, with a score of: 0.502758461345261
WordScorePoS
participatory0JJ
tax0.036961556179878NN
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experience0.0299586785922821NN
process0NN
decade0NN
produce0VB

Label: 16, with a score of: 0.495435913782544
WordScorePoS
participatory0.0811931365363091JJ
tax0.0811931365363091NN
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decade0NN
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Label: 13, with a score of: 0.481434725129833
WordScorePoS
participatory0JJ
tax0NN
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experience0.0335649154577099NN
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decade0.124232265906675NN
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Clear guidelines may be needed on the legal and judicial frameworks that govern the tools for participation

Label: 16, with a score of: 0.594192230173002
WordScorePoS
guideline0NN
legal0.0811931365363091JJ
framework0NN
tool0NN
participation0.0548954478802642NN

Label: 14, with a score of: 0.469520890387306
WordScorePoS
guideline0.0531983120572613NN
legal0.0401833161726777JJ
framework0.0141533244035107NN
tool0NN
participation0NN

Label: 1, with a score of: 0.407876636703214
WordScorePoS
guideline0NN
legal0JJ
framework0.0319966432858631NN
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participation0.0614198493690006NN

Label: 12, with a score of: 0.362916700498155
WordScorePoS
guideline0NN
legal0JJ
framework0.028867295332595NN
tool0.0542519672965169NN
participation0NN

And as more data is collected on performance and well being in cities this information must be readily available to all stakeholders see Box

Label: 11, with a score of: 0.931139857893742
WordScorePoS
datum0NN
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city0.607697021770652NN
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stakeholder0NN

Case Study Filling the Information Power Gap in Slums of Pune India is home to almost percent of the world slum population and one third of the world poor

Label: 11, with a score of: 0.705850272991791
WordScorePoS
study0NN
information0NN
power0NN
gap0NN
slum0.146277152173395NN
world0.00626002771693937NN
population0.0194583992186006NN
poor0.0229054731708393JJ

Label: 1, with a score of: 0.316533914070172
WordScorePoS
study0NN
information0NN
power0NN
gap0NN
slum0NN
world0.00257343720272548NN
population0NN
poor0.150659516460257JJ

Label: 9, with a score of: 0.362436430684947
WordScorePoS
study0NN
information0.0675706773907211NN
power0.0454743001434393NN
gap0.0602029956716497NN
slum0NN
world0.00257642711761688NN
population0NN
poor0JJ

a India urban population is set to double in the next years to million and the slum population is expected to grow even faster

Label: 11, with a score of: 0.812802056856464
WordScorePoS
urban0.27622591898666JJ
population0.0194583992186006NN
set0NN
double0RB
slum0.146277152173395NN
expect0VB
grow0.0229054731708393VB
faster0RBR

Label: 10, with a score of: 0.438889901055775
WordScorePoS
urban0.0488696285210061JJ
population0.0860640399817412NN
set0NN
double0RB
slum0NN
expect0VB
grow0.0405241466500776VB
faster0RBR

The country has advanced considerably over the past two decades in its national policies and its intent to create more inclusive urban growth but government programs still stumble at implementation because they are missing the planning practices that would empower the urban poor themselves

Label: 11, with a score of: 0.711275733548374
WordScorePoS
country0NN
advance0.0373517915079663NN
decade0.055245183797332NN
national0.00501208088147356JJ
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create0.0315914058171786VB
inclusive0.0583751976558017JJ
urban0.27622591898666JJ
growth0NN
government0NN
implementation0NN
plan0.0583751976558017NN
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empower0VB
poor0.0229054731708393JJ

Label: 8, with a score of: 0.298333238408147
WordScorePoS
country0NN
advance0NN
decade0NN
national0.0055149369084567JJ
policy0.0452169560888236NN
create0.0695218668857431VB
inclusive0.0214106369326177JJ
urban0JJ
growth0.208565600657229NN
government0NN
implementation0NN
plan0NN
practice0NN
empower0VB
poor0JJ

Label: 1, with a score of: 0.296505117361458
WordScorePoS
country0NN
advance0NN
decade0NN
national0.0164833460638534JJ
policy0.0450489732120807NN
create0.0519476926891779VB
inclusive0.0319966432858631JJ
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growth0.0519476926891779NN
government0NN
implementation0NN
plan0NN
practice0NN
empower0VB
poor0.150659516460257JJ

Label: 10, with a score of: 0.400328932056292
WordScorePoS
country0NN
advance0NN
decade0NN
national0.00886732612541978JJ
policy0.0848203862485078NN
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inclusive0.0172128079963482JJ
urban0.0488696285210061JJ
growth0.11178243319132NN
government0NN
implementation0.0475643836183697NN
plan0.0344256159926965NN
practice0.02794560829783NN
empower0.0396106020715138VB
poor0JJ

When urban development policies and programs plan for the poor without involving them slum residents continue to be marginalized and solutions crippled

Label: 11, with a score of: 0.743437926513014
WordScorePoS
urban0.27622591898666JJ
development0.0100241617629471NN
policy0.0136980135278128NN
plan0.0583751976558017NN
poor0.0229054731708393JJ
involve0VB
slum0.146277152173395NN
continue0.0537696560159885VB
marginalize0VB
solution0NN

Label: 1, with a score of: 0.271388418214275
WordScorePoS
urban0JJ
development0.0164833460638534NN
policy0.0450489732120807NN
plan0NN
poor0.150659516460257JJ
involve0VB
slum0NN
continue0VB
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Label: 10, with a score of: 0.393121289634014
WordScorePoS
urban0.0488696285210061JJ
development0.0177346522508396NN
policy0.0848203862485078NN
plan0.0344256159926965NN
poor0JJ
involve0.0488696285210061VB
slum0NN
continue0.0237821918091848VB
marginalize0.0488696285210061VB
solution0NN

Slum residents have long demonstrated their ability to organize learn from others contribute resources and implement solutions

Label: 4, with a score of: 0.509723739227847
WordScorePoS
slum0NN
ability0NN
organize0VB
learn0.244520809605025VB
contribute0VB
resource0NN
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solution0NN

Label: 11, with a score of: 0.455738907832956
WordScorePoS
slum0.146277152173395NN
ability0NN
organize0VB
learn0VB
contribute0VB
resource0.0449504361447405NN
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solution0NN

Label: 14, with a score of: 0.330716035500541
WordScorePoS
slum0NN
ability0NN
organize0VB
learn0VB
contribute0.0815049608642826VB
resource0.0572167664438909NN
implement0.0199268631468397VB
solution0NN

Label: 12, with a score of: 0.330181472447344
WordScorePoS
slum0NN
ability0.0409791940864438NN
organize0VB
learn0VB
contribute0.0554128417527413VB
resource0.0416785799329393NN
implement0.0203215381458133VB
solution0NN

There are some exceptional examples of such initiatives in India that can serve as models for the country and region for both state and non state actors

Label: 1, with a score of: 0.705148169074613
WordScorePoS
initiative0NN
serve0VB
country0NN
region0.184259548107002NN
actor0NN

Label: 3, with a score of: 0.47774554054497
WordScorePoS
initiative0NN
serve0VB
country0NN
region0.124837844401518NN
actor0NN

Label: 7, with a score of: 0.35385457428618
WordScorePoS
initiative0.0924643738905717NN
serve0VB
country0NN
region0NN
actor0NN

In CHF International an international NGO partnered with the Pune Municipal Corporation in India to implement program called Utthan meaning to rise from the bottom in Hindi with support from the Bill and Melinda Gates Foundation

Label: 0, with a score of: 1

Utthan collects information on the physical and socioeconomic conditions of Pune urban slums and uses this information to empower both residents and local government officials to undertake community development projects

Label: 11, with a score of: 0.676611772220448
WordScorePoS
information0NN
condition0NN
urban0.27622591898666JJ
slum0.146277152173395NN
empower0VB
local0.0194583992186006JJ
government0NN
undertake0VB
community0NN
development0.0100241617629471NN
project0NN

Label: 12, with a score of: 0.350607034249205
WordScorePoS
information0.0365348149782971NN
condition0.0409791940864438NN
urban0JJ
slum0NN
empower0VB
local0.0144336476662975JJ
government0NN
undertake0VB
community0.0169905820958638NN
development0.022306853314744NN
project0.0664302389559493NN

The Utthan program is distinct because data is being collected by an extensive network of over volunteers who reside in the slum communities

Label: 11, with a score of: 0.794087555614807
WordScorePoS
datum0NN
slum0.146277152173395NN
community0NN

Rather than simply extracting this survey information for municipal planning purposes the program gives the information about the community back to the volunteers

Label: 11, with a score of: 0.450610246435169
WordScorePoS
survey0NN
information0NN
municipal0.0731385760866977JJ
plan0.0583751976558017NN
community0NN

Label: 10, with a score of: 0.409055499128155
WordScorePoS
survey0.064698038783335NN
information0NN
municipal0JJ
plan0.0344256159926965NN
community0.0202620733250388NN

CHF International has trained the volunteers to organize community meetings prioritize their development interests and mobilize community and government resources to take action

Label: 17, with a score of: 0.528403506478141
WordScorePoS
international0.00648516099948979JJ
organize0VB
community0NN
development0.0469463607292177NN
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government0.048933054487825NN
resource0.0375923735942499NN
action0.0109843026967392NN

Label: 15, with a score of: 0.35917820587387
WordScorePoS
international0.00228545753063677JJ
organize0VB
community0.016202004209163NN
development0.0106357666046841NN
mobilize0.0633470357346975VB
government0NN
resource0.0317953333768269NN
action0.0232260916751625NN

Label: 16, with a score of: 0.358350148365571
WordScorePoS
international0.0189945307832458JJ
organize0.107490825192354VB
community0NN
development0.0368309542451156NN
mobilize0VB
government0NN
resource0NN
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To date the volunteers have collected detailed surveys in of Pune slums covering households approximately residents

Label: 11, with a score of: 0.729401219299141
WordScorePoS
survey0NN
slum0.146277152173395NN
cover0NN
household0NN
approximately0RB

Over two year period slums have participated in this micro planning process and all of them have mobilized community improvements both large and small covering physical improvements social issues and livelihoods

Label: 11, with a score of: 0.424533225829156
WordScorePoS
period0NN
slum0.146277152173395NN
micro0JJ
plan0.0583751976558017NN
process0NN
mobilize0VB
community0NN
improvement0NN
cover0NN
social0.0458109463416786JJ
issue0NN
livelihood0NN

Label: 15, with a score of: 0.457950457490288
WordScorePoS
period0NN
slum0NN
micro0.0390772412228479JJ
plan0.0137637438743113NN
process0.0316735178673487NN
mobilize0.0633470357346975VB
community0.016202004209163NN
improvement0NN
cover0.0390772412228479NN
social0JJ
issue0NN
livelihood0.0670377886812128NN

Label: 9, with a score of: 0.343022893422393
WordScorePoS
period0NN
slum0NN
micro0JJ
plan0NN
process0.0737171311497939NN
mobilize0VB
community0.0188543197850406NN
improvement0.0454743001434393NN
cover0NN
social0.0188543197850406JJ
issue0NN
livelihood0NN

The surveyed information is also aggregated into GIS model within the local government to inform planning of service delivery

Label: 0, with a score of: 1

This program demonstrates powerful model for institutionalizing more inclusive planning in slums and empowering slum residents to create change

Label: 11, with a score of: 0.679606398598113
WordScorePoS
inclusive0.0583751976558017JJ
plan0.0583751976558017NN
slum0.146277152173395NN
empower0VB
create0.0315914058171786VB
change0.0315914058171786NN

Label: 13, with a score of: 0.620992340511681
WordScorePoS
inclusive0JJ
plan0.0291713033871158NN
slum0NN
empower0VB
create0VB
change0.402565546215053NN

It also embodies some of the emerging concepts and approaches of open development which the World Bank has promoted namely more open governance citizen engagement in development collective action by citizens the co creation of development solutions and finally open data open knowledge and open solutions World Bank Institute

Label: 8, with a score of: 0.277395830012245
WordScorePoS
emerge0VB
approach0NN
development0.0055149369084567NN
world0.00172202218602071NN
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governance0NN
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datum0NN
knowledge0NN

The World Bank latest global poverty estimates calculate there are million people or about of the population living below the new international poverty line of

Label: 1, with a score of: 0.767199611378061
WordScorePoS
world0.00257343720272548NN
bank0NN
global0JJ
poverty0.269969049497485NN
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people0.0371911007748996NNS
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live0.0739258553811849VB
international0.00531301439641424JJ
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Label: 8, with a score of: 0.321736767321832
WordScorePoS
world0.00172202218602071NN
bank0NN
global0.0220597476338268JJ
poverty0.0722601961736204NN
estimate0NN
people0.0106656521351091NNS
population0.0428212738652355NN
live0.00989353561471291VB
international0.00355521737836969JJ
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per day in India

Label: 1, with a score of: 0.967928073819493
WordScorePoS
day0.25973846344589NN

The number of Indian poor constitutes of the global poor which is estimated at

Label: 1, with a score of: 0.762179423106589
WordScorePoS
poor0.150659516460257JJ
global0JJ
estimate0NN

Label: 6, with a score of: 0.348480080191308
WordScorePoS
poor0.0642010940957526JJ
global0.00936547854413134JJ
estimate0NN

Label: 2, with a score of: 0.307641418496891
WordScorePoS
poor0.0548141757796873JJ
global0.0119942111830662JJ
estimate0NN

billion people

Label: 1, with a score of: 0.574056633395636
WordScorePoS
people0.0371911007748996NNS

Label: 2, with a score of: 0.397824710342107
WordScorePoS
people0.0257736572183839NNS

Label: 6, with a score of: 0.372761699673373
WordScorePoS
people0.024149913320522NNS

Moreover India has million people or

Label: 1, with a score of: 0.574056633395636
WordScorePoS
people0.0371911007748996NNS

Label: 2, with a score of: 0.397824710342107
WordScorePoS
people0.0257736572183839NNS

Label: 6, with a score of: 0.372761699673373
WordScorePoS
people0.024149913320522NNS

of the population living below per day Sub Saharan Africa considered the world poorest region is better off in this respect with

Label: 1, with a score of: 0.830134998944778
WordScorePoS
population0NN
live0.0739258553811849VB
day0.25973846344589NN
saharan0.0442083771593746JJ
africa0IN
world0.00257343720272548NN
poorest0JJS
region0.184259548107002NN
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Label: 3, with a score of: 0.335539438461166
WordScorePoS
population0NN
live0.0300513417930436VB
day0.0175975629039954NN
saharan0.0299516555085695JJ
africa0IN
world0.00087176582269799NN
poorest0JJS
region0.124837844401518NN
respect0.0249430985430622NN

of its population million people below the per day level Chen and Ravallion

Label: 1, with a score of: 0.799883555699833
WordScorePoS
population0NN
people0.0371911007748996NNS
day0.25973846344589NN
level0.0113874611101829NN

BOX Mobile Phones Sweep Asia Asia is the leading region in terms of mobile phone accessibility

Label: 1, with a score of: 0.532819413305003
WordScorePoS
phone0NN
asia0IN
lead0NN
region0.184259548107002NN
term0NN

Label: 17, with a score of: 0.433154366081025
WordScorePoS
phone0NN
asia0IN
lead0NN
region0NN
term0.149793392961411NN

Label: 3, with a score of: 0.434781849068022
WordScorePoS
phone0NN
asia0IN
lead0.025518364538905NN
region0.124837844401518NN
term0NN

Inexpensive phones are already available to some

Label: 9, with a score of: 1
WordScorePoS
phone0.0602029956716497NN

billion people on the continent about half the population according to Business Monitor International

Label: 10, with a score of: 0.500495858707414
WordScorePoS
people0.00571634317201755NNS
continent0NN
population0.0860640399817412NN
business0NN
monitor0.0396106020715138NN
international0.00571634317201755JJ

Label: 9, with a score of: 0.409666815218958
WordScorePoS
people0.0186171553883251NNS
continent0NN
population0NN
business0.0909486002868787NN
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international0.00265959362690358JJ

Label: 2, with a score of: 0.387088404373544
WordScorePoS
people0.0257736572183839NNS
continent0.0440683498519077NN
population0.0310434135749871NN
business0NN
monitor0NN
international0.00515473144367677JJ

Label: 12, with a score of: 0.376020319909474
WordScorePoS
people0.0119834723221875NNS
continent0NN
population0.0144336476662975NN
business0.0409791940864438NN
monitor0.0332151194779746NN
international0.0023966944644375JJ

Third generation mobile data service which allows greater bandwidth for applications such as streaming video is growing rapidly as well with million phones having come online as of

Label: 9, with a score of: 0.746113011822822
WordScorePoS
generation0.0909486002868787NN
datum0.0454743001434393NN
service0.0148023490369527NN
grow0.0188543197850406VB
rapidly0RB
phone0.0602029956716497NN
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Label: 17, with a score of: 0.398052647615434
WordScorePoS
generation0NN
datum0.0739231123597559NN
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grow0VB
rapidly0RB
phone0NN
online0.048933054487825NN

Label: 12, with a score of: 0.397252236078351
WordScorePoS
generation0.0819583881728876NN
datum0NN
service0.00666957305782837NN
grow0.0339811641917276VB
rapidly0RB
phone0NN
online0NN

Mobile Penetration in Asia Selected Countries Forecast Average Annual Growth BMI Forecast No

Label: 10, with a score of: 0.686393096286111
WordScorePoS
asia0IN
country0NN
average0.146608885563018NN
growth0.11178243319132NN

Label: 8, with a score of: 0.554035596489963
WordScorePoS
asia0IN
country0NN
average0NN
growth0.208565600657229NN

Label: 13, with a score of: 0.440015658242209
WordScorePoS
asia0IN
country0NN
average0.1656430212089NN
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of Subscribers millions Mobile Penetration BMI Forecast Mobile Penetration Hong Kong

Label: 0, with a score of: 1

Singapore

Label: 0, with a score of: 1

Australia

Label: 0, with a score of: 1

Taiwan

Label: 0, with a score of: 1

Korea

Label: 0, with a score of: 1

Malaysia

Label: 0, with a score of: 1

Japan

Label: 0, with a score of: 1

Thailand

Label: 0, with a score of: 1

Philippines

Label: 0, with a score of: 1

China

Label: 0, with a score of: 1

Indonesia

Label: 0, with a score of: 1

Pakistan

Label: 0, with a score of: 1

Vietnam

Label: 0, with a score of: 1

India

Label: 0, with a score of: 1

Source Business Monitor International

Label: 9, with a score of: 0.578905258153077
WordScorePoS
source0.0135141354781442NN
business0.0909486002868787NN
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international0.00265959362690358JJ

Label: 17, with a score of: 0.418210076031244
WordScorePoS
source0.0109843026967392NN
business0NN
monitor0.0599173571845643NN
international0.00648516099948979JJ

Label: 12, with a score of: 0.413909383102819
WordScorePoS
source0NN
business0.0409791940864438NN
monitor0.0332151194779746NN
international0.0023966944644375JJ

Label: 6, with a score of: 0.347891985415491
WordScorePoS
source0.0613562156822703NN
business0NN
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international0.00301873916506525JJ

Participation through Information Technology and Social Media The availability of technology especially mobile telecommunications the Internet and social media has greatly changed the possibilities for citizen participation Annex

Label: 13, with a score of: 0.43079476405946
WordScorePoS
participation0NN
information0NN
technology0NN
social0JJ
media0NNS
availability0NN
internet0NN
change0.402565546215053NN

Label: 1, with a score of: 0.431070039483229
WordScorePoS
participation0.0614198493690006NN
information0NN
technology0.0269969049497485NN
social0.112994637345193JJ
media0NNS
availability0NN
internet0NN
change0NN

Label: 17, with a score of: 0.390071522106006
WordScorePoS
participation0NN
information0.0219686053934784NN
technology0.109843026967392NN
social0JJ
media0NNS
availability0.048933054487825NN
internet0.0739231123597559NN
change0NN

Label: 7, with a score of: 0.348410209309335
WordScorePoS
participation0NN
information0NN
technology0.109914925284606NN
social0JJ
media0NNS
availability0NN
internet0NN
change0.105749654845006NN

Label: 9, with a score of: 0.334866448051207
WordScorePoS
participation0NN
information0.0675706773907211NN
technology0.0810848128688654NN
social0.0188543197850406JJ
media0NNS
availability0NN
internet0.0454743001434393NN
change0NN

Mobile phones are becoming more and more ubiquitous as their price continues to drop Box and the use of social media is likewise spreading rapidly

Label: 10, with a score of: 0.473361850208162
WordScorePoS
phone0NN
price0NN
continue0.0237821918091848VB
social0.101310366625194JJ
media0NNS
spread0NN
rapidly0RB

Label: 1, with a score of: 0.427582194070954
WordScorePoS
phone0NN
price0NN
continue0VB
social0.112994637345193JJ
media0NNS
spread0NN
rapidly0RB

allows for mass collaboration by enabling many tomany connectivity on scale we have never seen before

Label: 0, with a score of: 1

Many see the shift toward collaborative problem solving as not only good but necessary for the complex challenges our societies face today

Label: 16, with a score of: 0.598827105867749
WordScorePoS
challenge0NN
society0.118536858482514NN

Label: 11, with a score of: 0.478782473856063
WordScorePoS
challenge0.0947742174515357NN
society0NN

Label: 8, with a score of: 0.474493561439956
WordScorePoS
challenge0.0347609334428716NN
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Nowhere is this more essential than in efforts toward sustainable cities which require not just improved infrastructure and regulations but also fundamental changes in behavior

Label: 11, with a score of: 0.846355346521382
WordScorePoS
essential0JJ
sustainable0.00782503464617421JJ
city0.607697021770652NN
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infrastructure0.0194583992186006NN
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Label: 9, with a score of: 0.290337314582074
WordScorePoS
essential0JJ
sustainable0.00901749491165909JJ
city0NN
require0.0260040237236563VB
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infrastructure0.192202909044225NN
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Smart cities are of limited value without smart citizens

Label: 11, with a score of: 0.978501176189312
WordScorePoS
city0.607697021770652NN
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Unlike traditional media which simply provides channel for mass dissemination social media Large scale collaboration is needed among governments the private sector academia civil society BUILDING SUSTAINABILITY IN AN URBANIZING WORLD FIG

Label: 17, with a score of: 0.756133908468498
WordScorePoS
traditional0JJ
media0NNS
dissemination0.048933054487825NN
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scale0NN
government0.048933054487825NN
private0.0898760357768464JJ
sector0.0459744293658165NN
civil0.0978661089756501JJ
society0.0359743605686701NN
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN

Label: 16, with a score of: 0.309043631955219
WordScorePoS
traditional0JJ
media0NNS
dissemination0NN
social0JJ
scale0NN
government0NN
private0JJ
sector0NN
civil0JJ
society0.118536858482514NN
building0.109790895760528NN
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Social Media Impact on Collective Action Facilitating Collaboration on Much Larger Scale ' Social media virtually eliminates communication and coordination costs making it far easier to exchange information to make group decisions on large scale and to integrate individual contributions into collective solution

Label: 10, with a score of: 0.492356063656757
WordScorePoS
social0.101310366625194JJ
media0NNS
impact0NN
action0.0145231653596925NN
facilitate0.02794560829783VB
scale0NN
eliminate0.0558912165956601VB
communication0NN
coordination0NN
cost0.0660824365173543NN
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decision0.02794560829783NN
integrate0VB
individual0.064698038783335JJ
contribution0NN
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Label: 9, with a score of: 0.389452373338256
WordScorePoS
social0.0188543197850406JJ
media0NNS
impact0.0160169090870187NN
action0.0135141354781442NN
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scale0.0368585655748969NN
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communication0.0922368138456871NN
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Empowering Individuals ' Technological progress has empowered individuals with the capacity to do what previously required large well resourced organizations

Label: 8, with a score of: 0.46543774501844
WordScorePoS
empower0VB
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technological0.0347609334428716JJ
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Label: 9, with a score of: 0.441358768357611
WordScorePoS
empower0.0368585655748969VB
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Label: 10, with a score of: 0.427263033283064
WordScorePoS
empower0.0396106020715138VB
individual0.064698038783335JJ
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progress0NN
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organization0.02794560829783NN

Podcasts for instance require no expensive broadcasting licenses or professional studios laptop will do

Label: 8, with a score of: 0.66670195543785
WordScorePoS
require0.104282800328615VB
expensive0JJ

Label: 12, with a score of: 0.646475443661785
WordScorePoS
require0.0468670845659596VB
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In this newly flattened world meritocracy rules as institutional advantages erode

Label: 16, with a score of: 0.745924524753857
WordScorePoS
newly0RB
world0NN
rule0.162386273072618NN
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Label: 17, with a score of: 0.528589681491856
WordScorePoS
newly0RB
world0.00418824502414673NN
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institutional0.036961556179878JJ

Label: 13, with a score of: 0.39660820246977
WordScorePoS
newly0RB
world0.00351929966767313NN
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and the public

Label: 5, with a score of: 0.640633878746864
WordScorePoS
public0.0727679293221751NN

Label: 11, with a score of: 0.513923230263676
WordScorePoS
public0.0583751976558017NN

Label: 17, with a score of: 0.34383816003256
WordScorePoS
public0.0390556786919513NN

Social media facilitates that collaboration by lowering costs empowering individuals and providing access to unprecedented amounts of information Figure

Label: 10, with a score of: 0.567619687268402
WordScorePoS
social0.101310366625194JJ
media0NNS
facilitate0.02794560829783VB
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individual0.064698038783335JJ
provide0VB
access0.00138439773401932NN
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information0NN
figure0NN

Label: 9, with a score of: 0.433257670752178
WordScorePoS
social0.0188543197850406JJ
media0NNS
facilitate0.0260040237236563VB
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individual0JJ
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access0.0128821355880844NN
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information0.0675706773907211NN
figure0.0602029956716497NN

Label: 1, with a score of: 0.394335116804889
WordScorePoS
social0.112994637345193JJ
media0NNS
facilitate0VB
cost0NN
empower0VB
individual0JJ
provide0.0295703421524739VB
access0.00514687440545096NN
amount0.0614198493690006NN
information0NN
figure0NN

As result collective action is increasingly taking the form of selforganized networks that use social media for peer to peer decision making in place of top down leadership and coordination

Label: 16, with a score of: 0.38788214232164
WordScorePoS
result0NN
action0NN
form0.158049144643351NN
social0JJ
media0NNS
decision0.0464294830712827NN
leadership0NN
coordination0NN

Label: 1, with a score of: 0.531816371025639
WordScorePoS
result0NN
action0.0269969049497485NN
form0.0884167543187491NN
social0.112994637345193JJ
media0NNS
decision0.0519476926891779NN
leadership0NN
coordination0NN

Label: 5, with a score of: 0.527862126954343
WordScorePoS
result0NN
action0.0409315617631NN
form0.100540298425703NN
social0.0285529457667019JJ
media0NNS
decision0.0393804437159645NN
leadership0.0688661930304451NN
coordination0NN

Label: 17, with a score of: 0.316689198650877
WordScorePoS
result0NN
action0.0109843026967392NN
form0NN
social0JJ
media0NNS
decision0.0211360962287062NN
leadership0.036961556179878NN
coordination0.0978661089756501NN

Governments and international organizations are starting to adapt to this new reality by experimenting with new models that tap into these networks

Label: 0, with a score of: 1

At more basic level networked technologies are being used in developing countries to support traditional service provision and facilitate interactions among citizens governments and businesses

Label: 9, with a score of: 0.514700116292623
WordScorePoS
basic0.0480507272610562JJ
level0NN
technology0.0810848128688654NN
develop0VB
country0NN
support0.0277502419802315NN
traditional0JJ
service0.0148023490369527NN
provision0NN
facilitate0.0260040237236563VB
government0NN
business0.0909486002868787NN

Label: 17, with a score of: 0.426043700486707
WordScorePoS
basic0JJ
level0.0138997399903163NN
technology0.109843026967392NN
develop0VB
country0NN
support0.0451108483130998NN
traditional0JJ
service0NN
provision0NN
facilitate0.0211360962287062VB
government0.048933054487825NN
business0NN

Label: 7, with a score of: 0.404329174675336
WordScorePoS
basic0JJ
level0NN
technology0.109914925284606NN
develop0VB
country0NN
support0.0188084899376888NN
traditional0JJ
service0.0451471467893167NN
provision0NN
facilitate0.0528748274225028VB
government0NN
business0NN

For example Malaysia Government Multipurpose Card MYKAD serves as common currency in electronic interactions at several levels of government UN Habitat and India has recently supplied some million tablets in rural areas to support education and health programs

Label: 4, with a score of: 0.520568621110222
WordScorePoS
government0NN
serve0VB
common0JJ
level0.0231525547935232NN
habitat0NN
supply0.0352060273355634NN
rural0JJ
support0NN
education0.244244978014045NN
health0NN

Label: 3, with a score of: 0.472145078412928
WordScorePoS
government0NN
serve0VB
common0.0615471563780609JJ
level0NN
habitat0NN
supply0NN
rural0JJ
support0.00625975725959118NN
education0.0152605842304358NN
health0.191387734041787NN

Label: 2, with a score of: 0.281205828383635
WordScorePoS
government0NN
serve0VB
common0JJ
level0.00552410547653725NN
habitat0NN
supply0NN
rural0.148975141598503JJ
support0.00896409157262106NN
education0NN
health0NN

A recent survey in India suggests that even in rural areas with particularly weak Internet service Enabling Real Time Collective Intelligence ' Social media and the move towards open data enable stakeholders to share information with one another at unprecedented speed and scale

Label: 2, with a score of: 0.339738453598821
WordScorePoS
survey0NN
suggest0VB
rural0.148975141598503JJ
internet0NN
service0.0143446978608332NN
enable0VB
time0.0182713919265625NN
social0JJ
media0NNS
move0NN
datum0NN
stakeholder0NN
share0.0365427838531249NN
information0.0130963126055467NN
scale0.0357189919948307NN

Label: 17, with a score of: 0.534931582175832
WordScorePoS
survey0NN
suggest0VB
rural0JJ
internet0.0739231123597559NN
service0NN
enable0.0499801157438617VB
time0NN
social0JJ
media0NNS
move0NN
datum0.0739231123597559NN
stakeholder0.0739231123597559NN
share0.0766240489430275NN
information0.0219686053934784NN
scale0NN

Label: 9, with a score of: 0.440089961710375
WordScorePoS
survey0NN
suggest0.0602029956716497VB
rural0JJ
internet0.0454743001434393NN
service0.0148023490369527NN
enable0VB
time0.0188543197850406NN
social0.0188543197850406JJ
media0NNS
move0NN
datum0.0454743001434393NN
stakeholder0NN
share0.0377086395700813NN
information0.0675706773907211NN
scale0.0368585655748969NN

Label: 10, with a score of: 0.340807885205749
WordScorePoS
survey0.064698038783335NN
suggest0VB
rural0.0330412182586772JJ
internet0NN
service0.00795378154685591NN
enable0VB
time0.0607862199751165NN
social0.101310366625194JJ
media0NNS
move0NN
datum0NN
stakeholder0NN
share0NN
information0NN
scale0NN

Indeed mobile equipped citizens can today complement vast arrays of digital sensors for real time information flows and full situational awareness

Label: 17, with a score of: 0.557701632280376
WordScorePoS
complement0.0978661089756501NN
vast0JJ
array0NN
digital0.048933054487825JJ
time0NN
information0.0219686053934784NN
full0JJ
awareness0NN

citizens express willingness to pay for better government Kalsi et al

Label: 5, with a score of: 0.621086389832139
WordScorePoS
pay0.078760887431929NN
government0NN

Technology is also enabling greater participation and accountability

Label: 17, with a score of: 0.670912128077685
WordScorePoS
technology0.109843026967392NN
enable0.0499801157438617VB
participation0NN
accountability0.048933054487825NN

Label: 5, with a score of: 0.430829241506872
WordScorePoS
technology0.0409315617631NN
enable0.0465610847355851VB
participation0.0465610847355851NN
accountability0NN

Label: 7, with a score of: 0.353250621632419
WordScorePoS
technology0.109914925284606NN
enable0VB
participation0NN
accountability0NN

The MapKibera

Label: 0, with a score of: 1

Org project is mapping the slum settlement of Kibera in Nairobi using an online platform managed by community facility

Label: 11, with a score of: 0.798800707291901
WordScorePoS
project0NN
slum0.146277152173395NN
settlement0.146277152173395NN
online0NN
platform0NN
manage0VB
community0NN
facility0NN

Incidents like crime and fire can be reported providing evidence for lobbying efforts to address these issues

Label: 16, with a score of: 0.78900338674742
WordScorePoS
crime0.214981650384708NN
report0NN
provide0.013214597504793VB
evidence0NN
issue0NN

Label: 10, with a score of: 0.447395364192716
WordScorePoS
crime0NN
report0NN
provide0VB
evidence0.12939607756667NN
issue0NN

In the United States several Web sites allow citizens to file complaints share information and communicate urban service deficiencies such as power outages or damaged facilities

Label: 11, with a score of: 0.689851415564048
WordScorePoS
unite0VB
share0NN
information0NN
urban0.27622591898666JJ
service0.0269743071558856NN
power0NN
facility0NN

Label: 17, with a score of: 0.392513710746977
WordScorePoS
unite0.036961556179878VB
share0.0766240489430275NN
information0.0219686053934784NN
urban0JJ
service0NN
power0.036961556179878NN
facility0NN

Label: 9, with a score of: 0.376678536987247
WordScorePoS
unite0VB
share0.0377086395700813NN
information0.0675706773907211NN
urban0JJ
service0.0148023490369527NN
power0.0454743001434393NN
facility0NN

CityForward

Label: 0, with a score of: 1

Org and SeeClickFix

Label: 0, with a score of: 1

Org are two examples

Label: 0, with a score of: 1

In developing countries mobile phones are especially important for empowering citizens

Label: 9, with a score of: 0.817241907053572
WordScorePoS
develop0VB
country0NN
phone0.0602029956716497NN
empower0.0368585655748969VB

Compared to Internet sites phone applications can have the same or similar interactive features but they do not require literacy

Label: 4, with a score of: 0.734275166201999
WordScorePoS
compare0VB
internet0NN
phone0NN
feature0NN
require0VB
literacy0.244520809605025NN

Label: 9, with a score of: 0.395427787705246
WordScorePoS
compare0VB
internet0.0454743001434393NN
phone0.0602029956716497NN
feature0NN
require0.0260040237236563VB
literacy0NN

Label: 8, with a score of: 0.313152367959973
WordScorePoS
compare0VB
internet0NN
phone0NN
feature0NN
require0.104282800328615VB
literacy0NN

Information exchanged on mobile phones is generally anonymous making them potent political mobilization tool

Label: 9, with a score of: 0.613066668859268
WordScorePoS
information0.0675706773907211NN
phone0.0602029956716497NN
political0.0368585655748969JJ
mobilization0NN
tool0NN

Label: 5, with a score of: 0.491931974507254
WordScorePoS
information0.02046578088155NN
phone0NN
political0.111637082206855JJ
mobilization0NN
tool0NN

Label: 1, with a score of: 0.33828641252791
WordScorePoS
information0NN
phone0NN
political0JJ
mobilization0.0908430554521382NN
tool0NN

Label: 12, with a score of: 0.338076859346505
WordScorePoS
information0.0365348149782971NN
phone0NN
political0JJ
mobilization0NN
tool0.0542519672965169NN

The growth of mobile phone PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS penetration has dramatically increased exchange between citizens and transformed the arena of political dialogue

Label: 17, with a score of: 0.619649188032638
WordScorePoS
growth0NN
phone0NN
institution0.0249900578719309NN
partnership0.3914644359026NN
increase0.00432344066632653NN
transform0VB
political0JJ

Label: 16, with a score of: 0.404202750684813
WordScorePoS
growth0NN
phone0NN
institution0.274477239401321NN
partnership0NN
increase0NN
transform0VB
political0JJ

Label: 8, with a score of: 0.37813436763401
WordScorePoS
growth0.208565600657229NN
phone0NN
institution0.0410992516792148NN
partnership0NN
increase0.00711043475673939NN
transform0VB
political0JJ

Label: 10, with a score of: 0.431458472094264
WordScorePoS
growth0.11178243319132NN
phone0NN
institution0.0991236547760315NN
partnership0NN
increase0.00285817158600878NN
transform0VB
political0.0792212041430275JJ

Besides messaging and community mobilization cameras and other features of low cost mobile phones open many new possibilities

Label: 1, with a score of: 0.478083614227509
WordScorePoS
community0NN
mobilization0.0908430554521382NN
feature0NN
cost0NN
phone0NN

One opportunity is the surveillance of public officials by mobile phone camera and video

Label: 5, with a score of: 0.509337115365239
WordScorePoS
opportunity0.02046578088155NN
public0.0727679293221751NN
phone0NN

Label: 9, with a score of: 0.490218367416856
WordScorePoS
opportunity0.0135141354781442NN
public0.0160169090870187NN
phone0.0602029956716497NN

The term sousveillance was coined by Steve Mann to capture the citizen initiated reporting of wrongdoing as well as the idea of watching from below Mann

Label: 17, with a score of: 0.746397381683939
WordScorePoS
term0.149793392961411NN
capture0NN
report0NN
idea0NN

Many NGOs have developed survey instruments based on open source software that can be formatted and deployed on mobile phones

Label: 0, with a score of: 1

Epi surveyor and Gatherdata start with basic mobile phone making use of text message based systems to gather and analyze real time data on health and services such as water and electricity Datadyne

Label: 6, with a score of: 0.696560050334679
WordScorePoS
basic0.0181797964452808JJ
phone0NN
base0NN
system0NN
time0NN
datum0NN
health0NN
service0.0084006124696649NN
water0.556409482163189NN
electricity0.0348974218717993NN

Label: 12, with a score of: 0.314426511464024
WordScorePoS
basic0.0144336476662975JJ
phone0NN
base0.0199423462679015NN
system0NN
time0.0169905820958638NN
datum0NN
health0.0339811641917276NN
service0.00666957305782837NN
water0.186896403054502NN
electricity0NN

Label: 3, with a score of: 0.301466427692068
WordScorePoS
basic0JJ
phone0NN
base0.0149758277542848NN
system0NN
time0.0127591822694525NN
datum0NN
health0.191387734041787NN
service0.0100171139310145NN
water0.0382775468083574NN
electricity0NN

Label: 9, with a score of: 0.330923632493185
WordScorePoS
basic0.0480507272610562JJ
phone0.0602029956716497NN
base0NN
system0NN
time0.0188543197850406NN
datum0.0454743001434393NN
health0.0377086395700813NN
service0.0148023490369527NN
water0.0377086395700813NN
electricity0.030745604615229NN

Innovative governments have begun to use interactive communication channels to engage citizens and organizations as partners in public problem solving see for instance http www

Label: 17, with a score of: 0.588140386469748
WordScorePoS
government0.048933054487825NN
communication0.0499801157438617NN
engage0VB
organization0.0422721924574123NN
public0.0390556786919513NN

Label: 9, with a score of: 0.50162636772659
WordScorePoS
government0NN
communication0.0922368138456871NN
engage0.0454743001434393VB
organization0NN
public0.0160169090870187NN

challenge

Label: 11, with a score of: 0.799666530083443
WordScorePoS
challenge0.0947742174515357NN

Label: 7, with a score of: 0.446136416745782
WordScorePoS
challenge0.0528748274225028NN

gov

Label: 0, with a score of: 1

Governments are also leveraging social media to augment their capacity and improve their responsiveness via crowdsourcing

Label: 10, with a score of: 0.435108836632055
WordScorePoS
government0NN
social0.101310366625194JJ
media0NNS
capacity0NN
improve0.0121171980355011VB

Label: 1, with a score of: 0.433448124783414
WordScorePoS
government0NN
social0.112994637345193JJ
media0NNS
capacity0NN
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Label: 17, with a score of: 0.431063898491646
WordScorePoS
government0.048933054487825NN
social0JJ
media0NNS
capacity0.0451108483130998NN
improve0.0183291958415182VB

Chicago new Snow Portal for example enables citizens to claim neighborhood streets for communityled snow removal to volunteer for Snow Corps that helps the disadvantaged and to share shovels and other equipment within their neighborhoods

Label: 0, with a score of: 1

Companies too have begun to realize the benefits of adopting more open and collaborative approach as they increasingly contribute to and launch open source projects and move toward consensus based standards for everything from data to processes

Label: 12, with a score of: 0.724620947216042
WordScorePoS
company0.108503934593034NN
realize0VB
adopt0.0169905820958638VB
approach0.0542519672965169NN
contribute0.0554128417527413VB
source0NN
project0.0664302389559493NN
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base0.0199423462679015NN
standard0.0409791940864438NN
datum0NN
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Label: 17, with a score of: 0.345065279938603
WordScorePoS
company0NN
realize0.048933054487825VB
adopt0.0153248097886055VB
approach0NN
contribute0.0249900578719309VB
source0.0109843026967392NN
project0NN
move0NN
consensus0NN
base0.017987180284335NN
standard0NN
datum0.0739231123597559NN
process0NN

While these local applications of social media hold much promise issues like climate change also call for collaboration at the global level across nations cities corporations civil organizations and ultimately individuals

Label: 13, with a score of: 0.683244730793381
WordScorePoS
local0.0145856516935579JJ
social0JJ
media0NNS
hold0NN
issue0.0335649154577099NN
climate0.291881734282273NN
change0.402565546215053NN
global0.0375696197697663JJ
level0.0415277415054218NN
nation0.0671298309154199NN
city0NN
civil0JJ
organization0NN
ultimately0RB
individual0JJ

Label: 11, with a score of: 0.568381246967863
WordScorePoS
local0.0194583992186006JJ
social0.0458109463416786JJ
media0NNS
hold0NN
issue0NN
climate0.0229054731708393NN
change0.0315914058171786NN
global0.00501208088147356JJ
level0.00692515656684925NN
nation0NN
city0.607697021770652NN
civil0JJ
organization0NN
ultimately0RB
individual0JJ

Label: 5, with a score of: 0.175388419690236
WordScorePoS
local0JJ
social0.0285529457667019JJ
media0NNS
hold0.18234260265061NN
issue0NN
climate0NN
change0NN
global0JJ
level0.0172651853471334NN
nation0NN
city0NN
civil0JJ
organization0NN
ultimately0RB
individual0JJ

Here too ICT and social media have critical role to play by facilitating the intensive information flows and transparency needed for day to day coordination knowledge sharing mutual accountability and trust

Label: 1, with a score of: 0.753540796199491
WordScorePoS
social0.112994637345193JJ
media0NNS
critical0JJ
facilitate0VB
intensive0JJ
information0NN
transparency0NN
day0.25973846344589NN
coordination0NN
knowledge0NN
share0NN
accountability0NN

Label: 17, with a score of: 0.421131586448841
WordScorePoS
social0JJ
media0NNS
critical0.036961556179878JJ
facilitate0.0211360962287062VB
intensive0JJ
information0.0219686053934784NN
transparency0NN
day0NN
coordination0.0978661089756501NN
knowledge0.0499801157438617NN
share0.0766240489430275NN
accountability0.048933054487825NN

And if information networks are central for governance of sustainable development they are equally important for sharing knowledge and encouraging innovation

Label: 9, with a score of: 0.523285596806185
WordScorePoS
information0.0675706773907211NN
central0JJ
governance0NN
sustainable0.00901749491165909JJ
development0.0330049940546426NN
share0.0377086395700813NN
knowledge0NN
encourage0.0260040237236563VB
innovation0.122982418460916NN

Label: 17, with a score of: 0.491256295927588
WordScorePoS
information0.0219686053934784NN
central0JJ
governance0NN
sustainable0.0115176738164035JJ
development0.0469463607292177NN
share0.0766240489430275NN
knowledge0.0499801157438617NN
encourage0.0211360962287062VB
innovation0.0499801157438617NN

Label: 8, with a score of: 0.334377301065876
WordScorePoS
information0NN
central0JJ
governance0NN
sustainable0.00688808874408283JJ
development0.0055149369084567NN
share0.0252035516550537NN
knowledge0NN
encourage0.0695218668857431VB
innovation0.0821985033584295NN

Label: 16, with a score of: 0.313756308463562
WordScorePoS
information0.0241291244414115NN
central0JJ
governance0.107490825192354NN
sustainable0.00920028227269801JJ
development0.0368309542451156NN
share0NN
knowledge0NN
encourage0VB
innovation0NN

Label: 2, with a score of: 0.305603386488529
WordScorePoS
information0.0130963126055467NN
central0.0440683498519077JJ
governance0NN
sustainable0.00374515576585943JJ
development0.0159922815774215NN
share0.0365427838531249NN
knowledge0.0595900566394012NN
encourage0VB
innovation0NN

Further Reading Annex summarizes policies for reducing emissions and increasing efficiency in cities around the world

Label: 11, with a score of: 0.898045115357743
WordScorePoS
policy0.0136980135278128NN
reduce0.0200483235258943VB
emission0.0373517915079663NN
increase0.00323105002784081NN
city0.607697021770652NN
world0.00626002771693937NN

Label: 13, with a score of: 0.297566500689733
WordScorePoS
policy0.0102677744436108NN
reduce0.00375696197697663VB
emission0.19598742448524NN
increase0.0145316075945746NN
city0NN
world0.00351929966767313NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD Learning and Innovation Key Messages ' Cities have formed learning partnerships with other cities and with national governments

Label: 11, with a score of: 0.812371027834384
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
learn0VB
innovation0.0373517915079663NN
key0NN
city0.607697021770652NN
form0NN
partnership0NN
national0.00501208088147356JJ
government0NN

Label: 17, with a score of: 0.421671857327611
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
learn0VB
innovation0.0499801157438617NN
key0NN
city0NN
form0NN
partnership0.3914644359026NN
national0.0134132459226336JJ
government0.048933054487825NN

Label: 4, with a score of: 0.29782151719157
WordScorePoS
building0NN
sustainability0NN
world0.0017440716962658NN
learn0.244520809605025VB
innovation0NN
key0NN
city0NN
form0NN
partnership0NN
national0JJ
government0NN

Label: 16, with a score of: 0.180412284580952
WordScorePoS
building0.109790895760528NN
sustainability0NN
world0NN
learn0VB
innovation0NN
key0NN
city0NN
form0.158049144643351NN
partnership0NN
national0.0294647633960925JJ
government0NN

A next could be to take triangular international partnerships to the city level

Label: 11, with a score of: 0.761515534180202
WordScorePoS
triangular0JJ
international0JJ
partnership0NN
city0.607697021770652NN
level0.00692515656684925NN

Label: 17, with a score of: 0.631536322184209
WordScorePoS
triangular0.0978661089756501JJ
international0.00648516099948979JJ
partnership0.3914644359026NN
city0NN
level0.0138997399903163NN

' number of private sector programs are targeting urban innovation

Label: 11, with a score of: 0.622193983615756
WordScorePoS
private0JJ
sector0NN
target0NN
urban0.27622591898666JJ
innovation0.0373517915079663NN

Label: 17, with a score of: 0.547050993708766
WordScorePoS
private0.0898760357768464JJ
sector0.0459744293658165NN
target0.0898760357768464NN
urban0JJ
innovation0.0499801157438617NN

Label: 9, with a score of: 0.429383936110063
WordScorePoS
private0.0368585655748969JJ
sector0.0565629593551219NN
target0NN
urban0JJ
innovation0.122982418460916NN

Cities are seen as important business clients as well as excellent vehicles to promote sustainability

Label: 11, with a score of: 0.873161399194852
WordScorePoS
city0.607697021770652NN
business0NN
vehicle0NN
promote0VB
sustainability0NN

Label: 12, with a score of: 0.450933235815064
WordScorePoS
city0NN
business0.0409791940864438NN
vehicle0.217007869186068NN
promote0.0148712355431627VB
sustainability0.0409791940864438NN

' In the academic community the system of urban research and practice needs to be optimized so that academic groups can contribute to larger whole and have access to the necessary funding to do so

Label: 11, with a score of: 0.670636276751899
WordScorePoS
community0NN
system0.0315914058171786NN
urban0.27622591898666JJ
research0NN
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contribute0VB
access0.00626002771693937NN

Label: 2, with a score of: 0.387671001768744
WordScorePoS
community0.0182713919265625NN
system0.0756001353333673NN
urban0JJ
research0.0252000451111224NN
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Label: 14, with a score of: 0.323659404865581
WordScorePoS
community0NN
system0.0229784274580034NN
urban0JJ
research0.0229784274580034NN
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contribute0.0815049608642826VB
access0.00113832851892715NN

Label: 12, with a score of: 0.309667543688871
WordScorePoS
community0.0169905820958638NN
system0NN
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' Multilateral institutions are best placed to organize and coordinate stakeholders and their knowledge

Label: 16, with a score of: 0.810763283449426
WordScorePoS
multilateral0JJ
institution0.274477239401321NN
organize0.107490825192354VB
coordinate0NN
stakeholder0NN
knowledge0NN

Label: 17, with a score of: 0.498359479866892
WordScorePoS
multilateral0.048933054487825JJ
institution0.0249900578719309NN
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coordinate0.036961556179878NN
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knowledge0.0499801157438617NN

The Urbanization Knowledge Platform an experimental information hub convened by the World Bank was first step in the process

Label: 11, with a score of: 0.575623926483734
WordScorePoS
urbanization0.146277152173395NN
knowledge0NN
platform0NN
information0NN
hub0.0731385760866977NN
world0.00626002771693937NN
bank0NN
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Label: 5, with a score of: 0.442051748608403
WordScorePoS
urbanization0NN
knowledge0NN
platform0.0911713013253052NN
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world0.00585260443759492NN
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Label: 2, with a score of: 0.442048416807909
WordScorePoS
urbanization0NN
knowledge0.0595900566394012NN
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information0.0130963126055467NN
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world0.0124838525528648NN
bank0.0881366997038154NN
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Label: 9, with a score of: 0.366949900540238
WordScorePoS
urbanization0NN
knowledge0NN
platform0NN
information0.0675706773907211NN
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There is no blueprint describing how to implement sustainable development in cities and no one sizefits all solution

Label: 11, with a score of: 0.944733867267997
WordScorePoS
implement0.0273960270556257VB
sustainable0.00782503464617421JJ
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Successful approaches are always context specific

Label: 17, with a score of: 0.782933060175487
WordScorePoS
successful0.048933054487825JJ
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context0.036961556179878NN

Label: 12, with a score of: 0.494509008723799
WordScorePoS
successful0JJ
approach0.0542519672965169NN
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Label: 13, with a score of: 0.377460810648276
WordScorePoS
successful0JJ
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Thus it is crucial for cities to be able to innovate and experiment with new institutions and policies

Label: 11, with a score of: 0.851207107215591
WordScorePoS
crucial0JJ
city0.607697021770652NN
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Label: 16, with a score of: 0.403565025771606
WordScorePoS
crucial0JJ
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institution0.274477239401321NN
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Improving markets prices or incentives may give boost to novel technologies but cities also need to learn approaches to sustainable development that are right for them and that make the most of their resources

Label: 11, with a score of: 0.735150637623195
WordScorePoS
improve0.0273960270556257VB
market0NN
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incentive0NN
technology0NN
city0.607697021770652NN
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sustainable0.00782503464617421JJ
development0.0100241617629471NN
resource0.0449504361447405NN

Label: 4, with a score of: 0.347613198319573
WordScorePoS
improve0.0305306222517556VB
market0NN
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incentive0NN
technology0.0182963616752599NN
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learn0.244520809605025VB
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sustainable0.00872035848132902JJ
development0.0279277626235394NN
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Label: 17, with a score of: 0.307420074329148
WordScorePoS
improve0.0183291958415182VB
market0.030649619577211NN
price0NN
incentive0.036961556179878NN
technology0.109843026967392NN
city0NN
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sustainable0.0115176738164035JJ
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For instance facing high risks from coastal floods developed countries with large budgets have innovated and invested significantly in structural coastal defenses

Label: 14, with a score of: 0.945417102215929
WordScorePoS
face0NN
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coastal0.265991560286306JJ
develop0VB
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With more limited resources Bangladesh has had to be innovative with early warning systems shelters and emergency planning

Label: 1, with a score of: 0.687476204735125
WordScorePoS
limited0.120266261535276JJ
resource0.0739147683583536NN
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system0.0519476926891779NN
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Label: 11, with a score of: 0.376844495746666
WordScorePoS
limited0JJ
resource0.0449504361447405NN
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system0.0315914058171786NN
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Label: 2, with a score of: 0.311315525979056
WordScorePoS
limited0JJ
resource0.0358563662904843NN
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system0.0756001353333673NN
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Given such differing contexts technology transfers to developing countries will sometimes not be sufficient

Label: 17, with a score of: 0.703910445593938
WordScorePoS
context0.036961556179878NN
technology0.109843026967392NN
transfer0.036961556179878NN
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Label: 7, with a score of: 0.421025681432929
WordScorePoS
context0NN
technology0.109914925284606NN
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country0NN

Label: 14, with a score of: 0.399326878625526
WordScorePoS
context0NN
technology0.0238835024181023NN
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Label: 9, with a score of: 0.310592838084356
WordScorePoS
context0NN
technology0.0810848128688654NN
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Cities should focus on how they can create their own responses to sustainability issues in accordance with the resources available to them

Label: 11, with a score of: 0.901298731480217
WordScorePoS
city0.607697021770652NN
focus0.0373517915079663NN
create0.0315914058171786VB
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resource0.0449504361447405NN

City specific knowledge and appropriate policies can grow from connections and exchanges between cities national governments the private sector http www

Label: 11, with a score of: 0.937932532071668
WordScorePoS
city0.607697021770652NN
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grow0.0229054731708393VB
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urbanknowledge

Label: 0, with a score of: 1

org and civil society

Label: 17, with a score of: 0.689833835658612
WordScorePoS
civil0.0978661089756501JJ
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Label: 16, with a score of: 0.610956731041927
WordScorePoS
civil0JJ
society0.118536858482514NN

Label: 8, with a score of: 0.304941671532536
WordScorePoS
civil0JJ
society0.0591643007226199NN

Multilateral and academic institutions also have important roles as knowledge hubs and sources of specialized expertise

Label: 16, with a score of: 0.729065071108863
WordScorePoS
multilateral0JJ
institution0.274477239401321NN
knowledge0NN
hub0NN
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Label: 17, with a score of: 0.488263319671657
WordScorePoS
multilateral0.048933054487825JJ
institution0.0249900578719309NN
knowledge0.0499801157438617NN
hub0NN
source0.0109843026967392NN
expertise0.048933054487825NN

Governments City governments today are learning from their peers across the world

Label: 11, with a score of: 0.913869633644466
WordScorePoS
government0NN
city0.607697021770652NN
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world0.00626002771693937NN

Label: 4, with a score of: 0.366563095972493
WordScorePoS
government0NN
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The relatively common sister city efforts are developing into more extensive and sustained partnerships among cities of various capacities Box

Label: 11, with a score of: 0.938477064568227
WordScorePoS
common0.055245183797332JJ
city0.607697021770652NN
develop0VB
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capacity0.0112376090361851NN

Label: 17, with a score of: 0.319621885322834
WordScorePoS
common0JJ
city0NN
develop0VB
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partnership0.3914644359026NN
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As discussed in Chapter transnational networks of cities are increasingly important channels for learning and collective action

Label: 11, with a score of: 0.91571676529945
WordScorePoS
transnational0JJ
city0.607697021770652NN
learn0VB
action0NN

Label: 4, with a score of: 0.36845960535976
WordScorePoS
transnational0JJ
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learn0.244520809605025VB
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At the same time dynamic partnerships between cities and their regional and national governments are becoming more frequent around the world

Label: 11, with a score of: 0.776992621944136
WordScorePoS
time0NN
partnership0NN
city0.607697021770652NN
regional0.0315914058171786JJ
national0.00501208088147356JJ
government0NN
world0.00626002771693937NN

Label: 17, with a score of: 0.597495527915747
WordScorePoS
time0NN
partnership0.3914644359026NN
city0NN
regional0.0422721924574123JJ
national0.0134132459226336JJ
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world0.00418824502414673NN

In many countries cities are growing up and gaining more forceful voice in national and international dialogues

Label: 11, with a score of: 0.959633181085256
WordScorePoS
country0NN
city0.607697021770652NN
grow0.0229054731708393VB
gain0.04477822029736NN
voice0NN
national0.00501208088147356JJ
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Mutually beneficial South South South north and North North partnerships and peer to peer learning can be augmented with additional ad hoc and permanent partners

Label: 17, with a score of: 0.865054784010327
WordScorePoS
mutually0RB
south0RB
north0RB
partnership0.3914644359026NN
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additional0.036961556179878JJ

Label: 4, with a score of: 0.493723303552936
WordScorePoS
mutually0RB
south0RB
north0RB
partnership0NN
learn0.244520809605025VB
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These triangular partnerships are already yielding benefits at the country level Figure and moving to the city scale is likely the next phase

Label: 11, with a score of: 0.729031658263082
WordScorePoS
triangular0JJ
partnership0NN
yield0NN
country0NN
level0.00692515656684925NN
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city0.607697021770652NN
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Label: 17, with a score of: 0.596904605782836
WordScorePoS
triangular0.0978661089756501JJ
partnership0.3914644359026NN
yield0NN
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level0.0138997399903163NN
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scale0NN
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BOX Cities Learning from Cities It is well accepted that creative cities have the upper hand in promoting competitiveness attracting business and dealing with pollution

Label: 11, with a score of: 0.982072891332337
WordScorePoS
city0.607697021770652NN
learn0VB
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competitiveness0NN
attract0VB
business0NN
pollution0.0315914058171786NN

Label: 4, with a score of: 0.135391422426132
WordScorePoS
city0NN
learn0.244520809605025VB
promote0.0111711050494158VB
competitiveness0NN
attract0VB
business0NN
pollution0NN

Barcelona Bilbao Curitiba and Seattle are much discussed examples

Label: 0, with a score of: 1

Not only do they implement better policies they also are role models in the way that they learn

Label: 4, with a score of: 0.768568221879659
WordScorePoS
implement0VB
policy0NN
learn0.244520809605025VB

Label: 17, with a score of: 0.345669658443094
WordScorePoS
implement0.0458229896037954VB
policy0.0641521854453136NN
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Label: 10, with a score of: 0.304690414312039
WordScorePoS
implement0.0121171980355011VB
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Seattle for instance is good learning city

Label: 11, with a score of: 0.925623363082234
WordScorePoS
learn0VB
city0.607697021770652NN

Label: 4, with a score of: 0.372445751783878
WordScorePoS
learn0.244520809605025VB
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City representatives have been visiting other cities since to build relationships and capture best practices

Label: 11, with a score of: 0.985487730900345
WordScorePoS
city0.607697021770652NN
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Seattle Trade Development Alliance is committed to keeping the city at the cutting edge of urban practices as well as establishing and maintaining relationships and promoting collective learning

Label: 11, with a score of: 0.896982301392661
WordScorePoS
trade0NN
development0.0100241617629471NN
city0.607697021770652NN
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Label: 4, with a score of: 0.268019277560717
WordScorePoS
trade0NN
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cut0NN
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This approach has been emulated by many other cities

Label: 11, with a score of: 0.993450567531736
WordScorePoS
approach0NN
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Dozens of cities have sought out best practices abroad

Label: 11, with a score of: 0.965255651480123
WordScorePoS
city0.607697021770652NN
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Bilbao launched its Guggenheim Museum full years after it digested lessons about industrial restructuring

Label: 9, with a score of: 0.964810632910936
WordScorePoS
full0.030745604615229JJ
industrial0.318320101004075JJ
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Lima long embattled over the role of private developers saw in London the impact of private sector partnerships

Label: 17, with a score of: 0.920901413438578
WordScorePoS
private0.0898760357768464JJ
impact0NN
sector0.0459744293658165NN
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There public authorities had created clear cut arrangements that guaranteed land and property rights in exchange for private investment in both private and public goods

Label: 5, with a score of: 0.543385782932625
WordScorePoS
public0.0727679293221751NN
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land0.024255976440725NN
property0.0558185411034276NN
rights0.078760887431929NNS
private0.0558185411034276JJ
investment0NN

Label: 1, with a score of: 0.479965583415085
WordScorePoS
public0NN
create0.0519476926891779VB
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land0.0319966432858631NN
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investment0.0376648791150643NN

Label: 17, with a score of: 0.436904071450481
WordScorePoS
public0.0390556786919513NN
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land0NN
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That lesson helped Lima to deliberate over large infrastructure projects in metropolitan development

Label: 9, with a score of: 0.715819836606185
WordScorePoS
help0NN
infrastructure0.192202909044225NN
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development0.0330049940546426NN

Label: 12, with a score of: 0.546242765868245
WordScorePoS
help0.0542519672965169NN
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FIG

Label: 0, with a score of: 1

Triangular Partnerships Leverage Members Comparative Advantages Source Reprinted from Gates

Label: 17, with a score of: 0.98656501825787
WordScorePoS
triangular0.0978661089756501JJ
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source0.0109843026967392NN

BOX Businesses Benefit from Sharing Information Campbell notes the importance of sharing and displaying private sector information related to sustainability goals

Label: 9, with a score of: 0.622410942915928
WordScorePoS
business0.0909486002868787NN
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related0.00925008066007716JJ
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Label: 17, with a score of: 0.569773487285163
WordScorePoS
business0NN
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private0.0898760357768464JJ
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Label: 5, with a score of: 0.324868531980855
WordScorePoS
business0NN
share0.0285529457667019NN
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private0.0558185411034276JJ
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goal0NN

More than companies around the world including of the world largest companies disclose data on greenhouse gas emissions climate risks and governance to the Carbon Disclosure Project launched in CDP

Label: 13, with a score of: 0.736191889319847
WordScorePoS
company0NN
world0.00351929966767313NN
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datum0NN
greenhouse0.0671298309154199NN
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carbon0.0559964069957828NN
project0.0335649154577099NN

Label: 12, with a score of: 0.424641059971561
WordScorePoS
company0.108503934593034NN
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climate0NN
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carbon0NN
project0.0664302389559493NN

Label: 7, with a score of: 0.40477519461873
WordScorePoS
company0NN
world0.00261936653839306NN
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greenhouse0.0749457204978913NN
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carbon0.0625160381065121NN
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Reporting on climate change issues seems to be correlated with corporate success companies with the most complete disclosure and or broadest action taken on climate change showed total returns about twice as large as control group for the period between January to May

Label: 13, with a score of: 0.898021880407923
WordScorePoS
report0NN
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Label: 7, with a score of: 0.304224661779136
WordScorePoS
report0NN
climate0.115011305411453NN
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In addition to seeking knowledge governments should set policies that support technological innovation

Label: 17, with a score of: 0.618079498262008
WordScorePoS
addition0NN
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knowledge0.0499801157438617NN
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innovation0.0499801157438617NN

Label: 9, with a score of: 0.525825174751755
WordScorePoS
addition0.0368585655748969NN
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innovation0.122982418460916NN

Label: 8, with a score of: 0.324482617710368
WordScorePoS
addition0NN
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policy0.0452169560888236NN
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innovation0.0821985033584295NN

Label: 1, with a score of: 0.319078936949896
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addition0NN
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According to the OECD the most appropriate type of policy instrument varies depending on the level of technology

Label: 17, with a score of: 0.480385483349986
WordScorePoS
oecd0NN
type0NN
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level0.0138997399903163NN
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Label: 5, with a score of: 0.412170084635895
WordScorePoS
oecd0NN
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level0.0172651853471334NN
technology0.0409315617631NN

Label: 12, with a score of: 0.342497571177737
WordScorePoS
oecd0.108503934593034NN
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In the case of promising but immature technologies government can support research and demonstration projects and determine which infrastructure and regulatory changes are needed to promote deployment

Label: 9, with a score of: 0.705745344979291
WordScorePoS
technology0.0810848128688654NN
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Label: 7, with a score of: 0.435637898696624
WordScorePoS
technology0.109914925284606NN
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Label: 17, with a score of: 0.387558031484597
WordScorePoS
technology0.109843026967392NN
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For example microgrids small scale smart grid systems are now being integrated in new ways at the neighborhood level

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WordScorePoS
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Label: 11, with a score of: 0.434928924904965
WordScorePoS
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Several pilot programs many with active involvement of local governments have been established to help inform technology standards and determine whether regulatory reforms are needed

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WordScorePoS
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Label: 5, with a score of: 0.364704708029399
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active0JJ
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For proven technologies that are ready to be deployed governments may provide technologyspecific support mechanisms to help jump start the market

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WordScorePoS
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Label: 9, with a score of: 0.309480400332655
WordScorePoS
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For alternative energy technologies that have become locally cost competitive but still lack market share government can promote public acceptance

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WordScorePoS
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Label: 12, with a score of: 0.2370283153044
WordScorePoS
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Label: 9, with a score of: 0.374724548748303
WordScorePoS
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For example San Francisco online Solar Map has been instrumental in documenting the efficacy of rooftop solar technology around the city OECD

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WordScorePoS
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Communities and Informal Networks Informal networks or clouds of trust seem to help cities learn at deeper level

Label: 11, with a score of: 0.899626659189422
WordScorePoS
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Label: 4, with a score of: 0.391795322484486
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These are trusted links among key actors in the community who have stake in its future

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WordScorePoS
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Label: 14, with a score of: 0.435849861348247
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Label: 2, with a score of: 0.416366086211084
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Label: 15, with a score of: 0.369209149935513
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Label: 7, with a score of: 0.334306111576892
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Elected leaders come and go but business civic and youth leaders who are involved in ongoing thinking about the city represent an important form of social capital

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WordScorePoS
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Label: 16, with a score of: 0.198957881970187
WordScorePoS
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They not only bridge gaps in connectedness and reinforce social norms Burt R

Label: 1, with a score of: 0.545012539671321
WordScorePoS
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Label: 10, with a score of: 0.488655227422425
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Label: 16, with a score of: 0.391622811375983
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Label: 9, with a score of: 0.381321001500436
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but also provide platform for learning and sustain the threads of continuity in place over time

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Label: 5, with a score of: 0.321289239254355
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Label: 10, with a score of: 0.3150667753918
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PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS Private Sector Partners in the local and international business community can often contribute essential knowledge and also benefit from learning processes in sustainable cities

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Label: 11, with a score of: 0.557671113137221
WordScorePoS
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Label: 16, with a score of: 0.300523141022223
WordScorePoS
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Label: 4, with a score of: 0.26212878281224
WordScorePoS
institution0NN
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Businesses that share information related to sustainability have been found to have higher returns Box and optimizing the flow of information and learning within city can create fertile environment for private investment and economic growth Box

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WordScorePoS
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Label: 8, with a score of: 0.423788431262491
WordScorePoS
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Label: 4, with a score of: 0.249447140265114
WordScorePoS
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Label: 9, with a score of: 0.384739557946607
WordScorePoS
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Businesses should be part of any city strategic discussion offering the best that they can contribute including financial and technical expertise and the city should encourage the link between local authorities stakeholders and firms

Label: 11, with a score of: 0.955579499274648
WordScorePoS
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The Urban Infrastructure Initiative UII was Knowledge City Creative City or Informational City

Label: 11, with a score of: 0.986750829346326
WordScorePoS
urban0.27622591898666JJ
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Label: 9, with a score of: 0.0895122632524319
WordScorePoS
urban0JJ
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'The knowledge city or knowledge based city has generally been defined by narrow focus on the relationship between academic institutions and businesses in cities

Label: 11, with a score of: 0.9700419127501
WordScorePoS
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Label: 16, with a score of: 0.143113466419676
WordScorePoS
knowledge0NN
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'The creative city enlarges the concept to include wider set of creative professions including the artists architects other designers and more broadly cultural industries

Label: 11, with a score of: 0.947540128984005
WordScorePoS
city0.607697021770652NN
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Label: 9, with a score of: 0.259984800252414
WordScorePoS
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'The smart city label is associated with use of networked infrastructure and ICT among other attributes see Chapter

Label: 11, with a score of: 0.939031545331777
WordScorePoS
city0.607697021770652NN
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Label: 9, with a score of: 0.287782882291556
WordScorePoS
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'Manuel Cassels uses the concept of the informational city which combines elements of the knowledge city and the creative city

Label: 11, with a score of: 0.993777217457802
WordScorePoS
city0.607697021770652NN
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The informational city has three key elements Stock ICT infrastructure for communicating information including connectivity cognitive infrastructure for transforming information into knowledge including humans and facilities and infrastructure other than ICT that provides firstclass leisure and retail opportunities

Label: 9, with a score of: 0.607300732445516
WordScorePoS
city0NN
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Label: 11, with a score of: 0.585324111569852
WordScorePoS
city0.607697021770652NN
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Label: 7, with a score of: 0.35891271517507
WordScorePoS
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BOX Several overlapping labels are used to describe cities that communicate and use knowledge in sophisticated way

Label: 11, with a score of: 0.97565808856894
WordScorePoS
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There are smart cities intelligent cities wired cities creative cities knowledge cities informational cities and many more see for example Hollands

Label: 11, with a score of: 0.995890538490466
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Informational cities that are successful in developing ICT infrastructure cognitive infrastructure and other amenities essentially become global cities Sassen

Label: 11, with a score of: 0.939264377395876
WordScorePoS
city0.607697021770652NN
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global0.00501208088147356JJ

Label: 9, with a score of: 0.286708583509478
WordScorePoS
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These are the cities that create or attract the businesses of the knowledge economy the capitalintensive service providers including banks stock exchanges and insurance companies the knowledge intensive high tech industries the industrial companies of the new economy such as computer manufacturers software developers and telecommunications and Internet firms as well as information service providers and creative companies ranging from architectural firms to advertising agencies

Label: 9, with a score of: 0.574694280030662
WordScorePoS
city0NN
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Label: 11, with a score of: 0.484268855322334
WordScorePoS
city0.607697021770652NN
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Achieving such economic success does not however make city sustainable

Label: 11, with a score of: 0.92145705136618
WordScorePoS
achieve0VB
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Label: 8, with a score of: 0.212034910450029
WordScorePoS
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Cities participating in the knowledge economy will grow or maintain their wealth as they will participate in the ownership of new assets Kennedy

Label: 11, with a score of: 0.903681377445329
WordScorePoS
city0.607697021770652NN
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But the mix of new knowledge and creativity does not necessarily lead to green products or to environmentally sustainable cities

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WordScorePoS
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This depends primarily on having suitable data such as the urban indicators discussed in Chapter and the political will and leadership to make green growth primary policy goal

Label: 11, with a score of: 0.486091588473658
WordScorePoS
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Label: 8, with a score of: 0.368571176191233
WordScorePoS
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Label: 10, with a score of: 0.471556133692425
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Label: 5, with a score of: 0.452653144956939
WordScorePoS
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BUILDING SUSTAINABILITY IN AN URBANIZING WORLD launched in by the WBCSD to demonstrate how business can unlock opportunities and develop practical solutions toward sustainable cities Box

Label: 11, with a score of: 0.88287570512874
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Label: 17, with a score of: 0.31151385714696
WordScorePoS
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For example the UII harnesses WBCSD project work in areas such as water electric utilities and energy efficiency in buildings along with many years of business expertise and experience working with cities

Label: 11, with a score of: 0.620198727055309
WordScorePoS
project0NN
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city0.607697021770652NN

Label: 6, with a score of: 0.517306277769065
WordScorePoS
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Label: 7, with a score of: 0.394565515208649
WordScorePoS
project0NN
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Label: 12, with a score of: 0.341636587992726
WordScorePoS
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Label: 17, with a score of: 0.191306088114534
WordScorePoS
project0NN
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It also brings in the best available experts from global companies

Label: 12, with a score of: 0.793318861566481
WordScorePoS
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Label: 11, with a score of: 0.473954718936265
WordScorePoS
bring0.0731385760866977VB
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The UII team then partners with selected cities around the world to explore and identify solutions for sustainable urbanization and urban infrastructure

Label: 11, with a score of: 0.960933484676107
WordScorePoS
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Label: 9, with a score of: 0.21739622104075
WordScorePoS
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BOX The IBM originated Urban Systems Collaborative see Box represents growing trend for major corporations to explore the future of cities

Label: 11, with a score of: 0.966696729529039
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Among the companies participating in the Sustainable Cities Partnership four of the most visible initiatives are Smarter Cities from IBM Connected Urban Development from Cisco Green City Indices from Siemens and the Livable Cities program from Philips

Label: 11, with a score of: 0.97910252243768
WordScorePoS
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Label: 17, with a score of: 0.172121650586651
WordScorePoS
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Private sector involvement is motivated by branding sales and philanthropy and reflects the importance that the corporate world gives to cities

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WordScorePoS
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For example Representatives from all four companies plus Alstom Veolia GDF Suez and PricewaterhouseCoopers attended the June workshop of the Sustainable Cities Partnership

Label: 11, with a score of: 0.806716722488409
WordScorePoS
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Label: 17, with a score of: 0.528157201492434
WordScorePoS
representative0NN
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The Urban Infrastructure Initiative The UII is an integrated approach with the ability to mobilize wide range of expertise and competencies from participating companies

Label: 11, with a score of: 0.600586994200098
WordScorePoS
urban0.27622591898666JJ
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Label: 9, with a score of: 0.393675140868309
WordScorePoS
urban0JJ
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Label: 12, with a score of: 0.434034511068653
WordScorePoS
urban0JJ
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Label: 17, with a score of: 0.324461445965131
WordScorePoS
urban0JJ
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UII member companies include Acciona Aecom AGC CEMEX EDF GDF Suez Honda Nissan Philips Schneider Electric Siemens TNT Toyota and UTC

Label: 0, with a score of: 1

The group co chairs are CEMEX GDF Suez Siemens and the WBCSD

Label: 0, with a score of: 1

Participating companies are sustainability pioneers who are aware of the interconnected nature of sustainable cities

Label: 11, with a score of: 0.877575615286781
WordScorePoS
company0NN
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Label: 12, with a score of: 0.468343787156882
WordScorePoS
company0.108503934593034NN
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An external Assurance Group comprised of six prominent experts in areas such as housing and development urban design and architecture reviews UII work provides quality approval and feedback and helps the team in its reflections on how to proceed

Label: 11, with a score of: 0.902692712903133
WordScorePoS
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Together the team handles most urban issues from waste to security energy water and sewage buildings and housing mobility logistics and health

Label: 11, with a score of: 0.585842824495746
WordScorePoS
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Label: 6, with a score of: 0.470506942551396
WordScorePoS
urban0JJ
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Label: 7, with a score of: 0.454449813576463
WordScorePoS
urban0JJ
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Label: 12, with a score of: 0.361837294288717
WordScorePoS
urban0JJ
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Label: 3, with a score of: 0.18672939960584
WordScorePoS
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Label: 17, with a score of: 0.139993543866719
WordScorePoS
urban0JJ
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energy0.0109843026967392NN
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building0.149940347231585NN
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They can also offer an end to end solution covering the entire life cycle of city infrastructure

Label: 11, with a score of: 0.861392279073994
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Label: 9, with a score of: 0.37043480362034
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Each UII project uses systemic approach to green urbanization and urban infrastructure while providing strong leadership and guidance

Label: 11, with a score of: 0.783901569524292
WordScorePoS
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Label: 9, with a score of: 0.373977213886598
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Label: 12, with a score of: 0.371954034808724
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UII projects begin with the identification of cities that are planning sustainability projects with possible sponsors or financers dialogue with city officials to understand their vision and main challenges determining and prioritizing solutions to address the main issues identifying tangible projects and measures and involving the appropriate experts from the UII

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To date the UII core team has worked with Philadelphia Surabaya Indonesia Tilburg the Netherlands Turku Finland and four cities in the Indian state of Gujarat

Label: 11, with a score of: 0.997380969185671
WordScorePoS
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UII will also work with cities in China and Japan

Label: 11, with a score of: 0.997380969185672
WordScorePoS
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The approaches vary across cities

Label: 11, with a score of: 0.993450567531736
WordScorePoS
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Outputs include advances in biogas production building automation cutting energy used by systems such as heating ventilation and air conditioning through electronic communication among equipment energy performance in public lighting and public buildings green logistics and reducing congestion

Label: 7, with a score of: 0.738068002842919
WordScorePoS
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Label: 12, with a score of: 0.352583082398464
WordScorePoS
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Label: 17, with a score of: 0.286808835408875
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Label: 11, with a score of: 0.368115343907756
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The UII key lessons thus far are that there is great value in involving business in the early stages of city sustainability planning cities must also create the right framework conditions and incentives to attract the necessary investments and multi stakeholder expertise is essential to help transform cities sustainability visions into effective and affordable plans

Label: 11, with a score of: 0.907272542443254
WordScorePoS
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PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS IBM Smarter Cities program is part of strategy to associate the company with capacity to generate system level solutions to complex problems

Label: 11, with a score of: 0.674512155840279
WordScorePoS
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Label: 17, with a score of: 0.527942008043102
WordScorePoS
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Label: 16, with a score of: 0.36119590047514
WordScorePoS
institution0.274477239401321NN
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Once the program was started IBM business and research divisions got involved compiling the company many city based consulting contracts into package of practice that could be studied generalized and replicated

Label: 11, with a score of: 0.815186010756889
WordScorePoS
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Label: 12, with a score of: 0.43151881069993
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Cisco Systems has an internal think tank the Internet Business Solutions Group IBSG charged with identifying long term commercial opportunities for their core business of networking hardware and software

Label: 17, with a score of: 0.529848544022553
WordScorePoS
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Label: 9, with a score of: 0.601024289663069
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One of IBSG programs Connected Urban Development CUD began in through collaboration with the cities of Amsterdam San Francisco and Seoul as well as other government and corporate partners

Label: 11, with a score of: 0.986744716182296
WordScorePoS
urban0.27622591898666JJ
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CUD created Web sites called Urban EcoMaps that could educate city dwellers about ways to reduce their environmental impacts

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WordScorePoS
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As the CUD program expanded with the involvement of other cities it gave rise to broader initiatives including Smart and Connected Communities and Planetary Skin

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In Cisco handed the leadership of CUD to Metropolis and the Climate Group

Label: 13, with a score of: 0.883527062726255
WordScorePoS
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Label: 7, with a score of: 0.348139636419398
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Like IBM Cisco has convened series of international conferences to share the potential of these programs with leaders from development banks scholars and city and environment focused NGOs

Label: 11, with a score of: 0.837070724773841
WordScorePoS
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Label: 5, with a score of: 0.279145877262767
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While IBM and Cisco focus on information flows within urban systems Siemens core business is targeted to the creation and maintenance of urban infrastructure

Label: 11, with a score of: 0.779793309410805
WordScorePoS
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Label: 9, with a score of: 0.464171705133353
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Label: 8, with a score of: 0.24585803268155
WordScorePoS
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In responding to the growing interest in green built environment Siemens created special division Infrastructure and Cities pulling together many of its city related offices

Label: 11, with a score of: 0.945903355789098
WordScorePoS
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Label: 9, with a score of: 0.212583946670246
WordScorePoS
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Siemens is well positioned to help create solutions for urban issues such as greenhouse gas reduction cleaner transportation systems and health care delivery in cities of the developing world

Label: 11, with a score of: 0.84664306721471
WordScorePoS
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Label: 3, with a score of: 0.271629465711801
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Its Green City Indices Annex are among the urban datagathering programs reviewed in Chapter

Label: 11, with a score of: 0.992735569442953
WordScorePoS
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Philips has also chosen focused approach in line with its particular strengths

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Label: 11, with a score of: 0.521198425701825
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Label: 9, with a score of: 0.429017192369219
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Its Livable Cities program is centered on how lighting can influence the quality of city life improve public safety increase energy efficiency and enhance mental health

Label: 11, with a score of: 0.88018799844919
WordScorePoS
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Label: 7, with a score of: 0.338472693821967
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Label: 3, with a score of: 0.18274997705895
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Label: 12, with a score of: 0.149115107540983
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Similarly PricewaterhouseCoopers uses its accounting expertise to lead program that calculates the GDP of cities

Label: 11, with a score of: 0.958335965865419
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Other notable corporate partners include Alstom and Veolia Environment

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Alstom focuses on urban energy and transport infrastructure and how ICT can help to optimize efficiency and reduce carbon dioxide emissions smart city technology is discussed in Chapter

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WordScorePoS
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Label: 7, with a score of: 0.519482194856425
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Label: 9, with a score of: 0.251520406835499
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Label: 13, with a score of: 0.218285485062489
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Label: 12, with a score of: 0.173521863051603
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The company has taken the lead in the first international development program for carbon neutral eco cities and runs dozens of pilot programs to test new technologies for smart cities in partnership with local utilities

Label: 11, with a score of: 0.879997505806935
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Label: 17, with a score of: 0.389653843506227
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In Alstom formed venture company named EMBIX with France Bouygues group to provide energy management services for eco cities

Label: 11, with a score of: 0.736125960626125
WordScorePoS
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Label: 7, with a score of: 0.509654368891994
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Label: 12, with a score of: 0.286215040874763
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Label: 16, with a score of: 0.161710051423022
WordScorePoS
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Veolia Environment focuses on urban systems management

Label: 11, with a score of: 0.885738550622955
WordScorePoS
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Their four divisions water energy waste and transportation cover all the service needs of cities

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Since the beginning of the decade Veolia has pushed systems approach to urban sustainability and supported number of research centers and collaborations dealing with such topics as city operations human behavior and attitudes

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In sum large corporations are aware that cities are important clients for their services as well http smartercities

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aspx http www

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com en BUILDING SUSTAINABILITY IN AN URBANIZING WORLD as excellent vehicles to promote sustainability

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Some of the initiatives may overlap and there is competition among consulting firms to advise city management

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Although competition is healthy in principle the development of parallel frameworks and indicators may result in dispersion of efforts and difficulty in comparing performance

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Over time however there will likely be specialization with different firms offering unique products in support of sustainable cities

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Academia BOX Much urban innovation to date has come from city officials practicing planners and architects nonprofit foundations and corporations that serve city governments and citizens

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Conspicuously absent from this list are university scientists and engineers

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This minimal role can be attributed in part to the reward system of academia which encourages research in areas that receive major federal funding

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Because cities tend to fall through the disciplinary cracks faculty interested in addressing urban sustainability problems have not had many opportunities to obtain the largest and most prestigious grants from agencies like the European Science Foundation or the U

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National Science Foundation or from the most prominent private foundations

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Nonetheless the growing emphasis on sustainability in colleges and universities has provided context in which urban problems can be more widely addressed

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An increasing number of universities have cross cutting sustainability initiatives that can help quantify the economic environmental and social impacts of cities

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In particular ways to measure urban metabolism see Chapter and carbon footprints were developed in universities and then moved into practice among cities

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Some academic institutions have been able to apply more specialized expertise from space flight to climate modeling to help cities address sustainability issues Box

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Channeling Specialized Expertise in Academia There are many examples of how universities exploit unusual expertise to advance their urban agendas

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MIT SENSEable City Laba and the Centre for Advanced Spatial Analysis at University College Londonb have been pioneers in the use of cell phone positional information and social media traffic to make complex urban dynamics more tangible

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These groups are emerging as the preferred academic partners for many cities and companies seeking to exploit these new technological approaches

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Arizona State University Mars Space Flight Facilityc has been designing and controlling instruments that map Mars and other planetary bodies for more than years

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Over the past decade they have turned their unique expertise in remote sensing and globalscale data management toward the monitoring and modeling of cities

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With NASA funding corporate aerospace partnerships and collaborative history with Google they are developing an urban information system called Earth which will allow virtually any type of urban data to be combined co registered searched and analyzed

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Finally in London the multi university Tyndall Centre for Climate Impact Research has worked unusually closely with city officials to analyze how climate change may affect different aspects of the city habitability ranging from flooding to the urban heatisland effect to traffic congestion

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d The Tyndall London methodology is now being considered for adoption by other cities

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http senseable

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uk info pdf engineeringcites

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pdf PART III THE ROLE OF INSTITUTIONS AND PARTNERSHIPS Urban universities that see the improvement of their local environments as part of their training research and service missions are best positioned to partner with government agencies and officials

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Such collaborations are becoming increasingly common especially as municipal budgets continue to be cut

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One good example is the Future Cities Centre being developed in London by group of universities and private sector entities including University College London Cisco and Arup

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Chicago and Portland offer similar examples

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The University of Illinois Chicago established the Great Cities Institute GCI in as focal point for studies of Chicago that also have relevance to other major cities around the world

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In its nearly years of existence the GCI has partnered with several hundred local regional national and global private and public sector organizations

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In conjunction with strong mayoral and corporate leadership the GCI has helped place Chicago at the forefront of cities coordinating innovative policy thinking and action

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Portland State University has worked with the City of Portland the Metro Regional Government and local and state agencies to bring the green urban innovations for which their region is well known to other cities across the United States with the assistance of other members of the Coalition of Urban Serving Universities

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As with competing corporations universities tend to have difficulty sharing the limelight with each other especially when reputation and funding are at stake

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Schools that establish partnerships with individual cities foundations or companies prefer not to give up or share those exclusive pipelines and competition for large grants is always fierce

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Hence optimizing the system of urban research http www

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imperial consultants

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uk news imperial college london ucl and cisco create future cities centre london http www

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org and practice so that each academic group can contribute to larger whole rather than trying to replicate what others are doing requires an unusual level of cooperation

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Two circumstances could foster system of academic collaboration

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If globally oriented network initiative were to become firmly established and funded for example ICLEI STAR index each urban oriented university could team up with its local municipal partner to assure that their city has prominent role in the collective enterprise

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Second if university has widely acknowledged and uniquely valued asset others might be more willing to include them in any potential consortia

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In more traditional scientific disciplines the establishment of major centers institutes or facilities through concentrated government funding has provided the kind of focus that has in turn allowed more collaborative community to emerge

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No such academically based initiative has yet been created in the United States

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Large scale over million per year funding of an urban focused Science and Technology Center or Engineering Research Center by the U

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National Science Foundation could have such catalyzing effect

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The Development Community fundamental difficulty associated with coordinating the different types of urban information and programs outlined above is the lack of an obvious organization that has the necessary authority and resources to forge consensus

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The budgets of virtually all the world city governments are being tightly squeezed

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Academic researchers at least in the United States have been unsuccessful in convincing funding agencies that cities are worth major investments

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Although many cash rich corporations express interest in promoting the understanding of cities they have generally been unwilling to commit their funds to urban research BUILDING SUSTAINABILITY IN AN URBANIZING WORLD programs that mostly benefit the public rather than their companies share value

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Some smaller philanthropic foundations have begun to support urban research programs but only at modest funding levels

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Arguably the only relevant players that have global reach and maintain access to sizeable pools of capital are multilateral development banks MDBs such as the World Bank Inter American Development Bank and Asian Development Bank and branches of the United Nations such as UN Habitat and UNESCO

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These organizations have history of working collaboratively to promote regional socioeconomic objectives including infrastructure development and institution building

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Some examples of successful collaboration efforts among partners which have yielded useful tools and platforms for city learning include the Global Protocol for City and Community GHG Accounting GPC and the Knowledge Centre on Cities and Climate Change

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The GPC was developed by ICLEI WRI the World Bank UN Habitat and UNEP to serve as the global framework for accounting and reporting city and community scale GHG emissions

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The GPC is expected to help cities and communities measure their GHG emissions to ensure that all emissions are being accounted for between different government municipalities

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The Knowledge Centre on Cities and Climate Change C is the product of Joint Work Programme between UNEP Cities Alliance UN Habitat and the World Bank

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The C is platform for sharing experiences and best practices as well as facilitating exchange of innovative initiatives

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It provides access to hundreds of publications and reports which are mapped to specific cities countries and regions

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ghgprotocol

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org city accounting http www

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citiesandclimatechange

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org The difficulty in getting the support of MDBs or the UN for urban initiatives is that they have tended to focus on countries or regions rather than cities or metropolitan areas

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Yet in the past few years the banks have been showing growing recognition of the need for more urbanscale actions

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They also have the credibility and convening authority that most members of other sectors lack

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A consortium of development banks could bring together representatives from federal state and municipal governments corporations NGOs philanthropies and universities to design grand scheme for urban information management and policy creation in support of global sustainability objectives

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Workshops on urban issues have already spawned multitude of knowledge networks and follow up initiatives but these have gained little traction in the absence of central coordination Box

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Further Reading Annex describes Siemens Green City Index series in detail

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Annex is detailed discussion of how the cities use of government services relates to both their ICT capabilities and their growing power as global players

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Annex reviews pilot programs for innovative technology services and business models and discusses general issues and lessons for such pilots

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Annex discusses how city planning could evolve with the growth of ICT and the infrastructure and capacities required for this

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Annex describes the World Bank Eco Cities Initiative which helps cities design development pathways for both ecological and economic sustainability

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Organizing Information from Urban Conferences New meetings commonly propose new knowledge hubs

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For example the Urban Systems Collaborative USC is an ad hoc initiative that grew out of an IBM sponsored symposium on Smarter Cities held in New York City in May

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The USC started as series of bi weekly conference calls with participants and then evolved to include specific projects such as student competition Web based reference repository and series of webinars

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Contributors BOX Interest in tools and data flows for urban sustainability continues to expand rapidly among government academic corporate and NGO players

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In fact the proliferation of well intentioned urban solutions workshops and strategic plans from all these sectors is becoming an unanticipated threat to achieving sustainability goals nobody can keep track of all of them let alone determine how they might be best integrated

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were encouraged to invite others to join interact on dedicated wiki and propose new components

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There was no identified funding source and labor to set up the constituent programs was voluntary

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Hundreds of other large and small urban themed conferences in recent years have compiled presentation materials online set up informal networks among participants and proposed the creation of follow on urban initiatives

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The majority of these efforts become dead ends largely unreferenced and forgotten even by the conveners

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Clearly there is pressing need to establish some sort of clearinghouse for conferences on cities and the products they generate

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This requires identification and screening by some organization confederation or Web site and willingness on the part of the community to coordinate and reduce the number of meetings they hold

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Photo Shutterstock PART IV The Path Forward Photo Jerry Kurniawan World Bank PART IV THE PATH FORWARD Next Steps Toward Sustainable Cities In just years cities will need to build the infrastructure for an additional

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billion people Annex

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As we have seen power water waste and transportation systems need to be created as well as local economies governance systems and jobs

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Label: 12, with a score of: 0.448299774253336
WordScorePoS
power0NN
water0.186896403054502NN
waste0.138532104381853NN
transportation0.0332151194779746NN
system0NN
create0.0234335422829798VB
local0.0144336476662975JJ
economy0NN
governance0NN
job0.0203215381458133NN

Label: 8, with a score of: 0.332092370928373
WordScorePoS
power0NN
water0NN
waste0NN
transportation0NN
system0NN
create0.0695218668857431VB
local0.0214106369326177JJ
economy0.0821985033584295NN
governance0NN
job0.135650868266471NN

This city building task is enormous

Label: 11, with a score of: 0.961827549902319
WordScorePoS
city0.607697021770652NN
building0.0747035830159326NN

Label: 17, with a score of: 0.211337381294322
WordScorePoS
city0NN
building0.149940347231585NN

Never has humanity faced as large challenge especially since many natural ecosystems are already well beyond the sustainable carrying capacity of today

Label: 15, with a score of: 0.525769280695738
WordScorePoS
humanity0NN
face0NN
challenge0.0223459295604043NN
natural0.016202004209163JJ
ecosystem0.134075577362426NN
sustainable0.00664197120025784JJ
capacity0.0158976666884135NN

Label: 11, with a score of: 0.489016148302308
WordScorePoS
humanity0.04477822029736NN
face0NN
challenge0.0947742174515357NN
natural0.0229054731708393JJ
ecosystem0NN
sustainable0.00782503464617421JJ
capacity0.0112376090361851NN

Label: 12, with a score of: 0.356169174685935
WordScorePoS
humanity0.0332151194779746NN
face0NN
challenge0NN
natural0.0509717462875914JJ
ecosystem0.0234335422829798NN
sustainable0.0162522423871414JJ
capacity0.00833571598658786NN

Label: 9, with a score of: 0.348666710979332
WordScorePoS
humanity0NN
face0.120405991343299NN
challenge0NN
natural0JJ
ecosystem0NN
sustainable0.00901749491165909JJ
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billion urban residents

Label: 11, with a score of: 0.984707888374301
WordScorePoS
urban0.27622591898666JJ

Sustainably building and managing new cities and retrofitting existing urban areas are imperative

Label: 11, with a score of: 0.962219588905272
WordScorePoS
sustainably0RB
building0.0747035830159326NN
manage0VB
city0.607697021770652NN
exist0.04477822029736VB
urban0.27622591898666JJ

Label: 17, with a score of: 0.201244009723062
WordScorePoS
sustainably0RB
building0.149940347231585NN
manage0VB
city0NN
exist0.0599173571845643VB
urban0JJ

There are only few decades to ensure that the next wave of urbanization does not lock in the limitations of most of today cities

Label: 11, with a score of: 0.968398419705896
WordScorePoS
decade0.055245183797332NN
ensure0.00501208088147356VB
urbanization0.146277152173395NN
lock0NN
city0.607697021770652NN

Cities are where everything comes together our ideas our culture our economies our aspirations and so too our impacts on the planet our vulnerabilities and increasingly much of our inequality

Label: 11, with a score of: 0.721152726051202
WordScorePoS
city0.607697021770652NN
idea0.0731385760866977NN
culture0.0373517915079663NN
economy0NN
impact0.0194583992186006NN
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vulnerability0NN
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Label: 10, with a score of: 0.621100347711549
WordScorePoS
city0NN
idea0NN
culture0NN
economy0NN
impact0NN
planet0NN
vulnerability0NN
inequality0.635305170773079NN

Challenge Green and inclusive growth can proceed only if it is well and truly anchored at the city level

Label: 11, with a score of: 0.902259678326107
WordScorePoS
challenge0.0947742174515357NN
green0.04477822029736JJ
inclusive0.0583751976558017JJ
growth0NN
city0.607697021770652NN
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Label: 8, with a score of: 0.302426809417649
WordScorePoS
challenge0.0347609334428716NN
green0JJ
inclusive0.0214106369326177JJ
growth0.208565600657229NN
city0NN
level0.00761994916892276NN

The Sustainable Cities Partnership is just one example of how the issue of cities can bring together disparate collection of stakeholders all of whom have much to gain by participating and much to contribute to sustainable cities

Label: 11, with a score of: 0.947885815356836
WordScorePoS
sustainable0.00782503464617421JJ
city0.607697021770652NN
partnership0NN
issue0NN
bring0.0731385760866977VB
collection0NN
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gain0.04477822029736NN
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Label: 17, with a score of: 0.286936823314966
WordScorePoS
sustainable0.0115176738164035JJ
city0NN
partnership0.3914644359026NN
issue0.0299586785922821NN
bring0VB
collection0.048933054487825NN
stakeholder0.0739231123597559NN
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contribute0.0249900578719309VB

Next Steps for Cities and Their Partners The Urbanization Knowledge Platform held extensive consultations with city leaders development practitioners policymakers and academics in countries and regions

Label: 11, with a score of: 0.945159662190569
WordScorePoS
city0.607697021770652NN
urbanization0.146277152173395NN
knowledge0NN
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development0.0100241617629471NN
country0NN
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Label: 5, with a score of: 0.229156112836046
WordScorePoS
city0NN
urbanization0NN
knowledge0NN
platform0.0911713013253052NN
hold0.18234260265061NN
development0.0124956749436837NN
country0NN
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Label: 1, with a score of: 0.138320863307853
WordScorePoS
city0NN
urbanization0NN
knowledge0NN
platform0NN
hold0NN
development0.0164833460638534NN
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The goal was to discuss stakeholders issues and concerns about enhancing citizen welfare the economic contributions of urbanization and sustainable urban development

Label: 11, with a score of: 0.738347621611585
WordScorePoS
goal0NN
stakeholder0NN
issue0NN
enhance0.0112376090361851VB
welfare0NN
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contribution0NN
urbanization0.146277152173395NN
sustainable0.00782503464617421JJ
urban0.27622591898666JJ
development0.0100241617629471NN

Label: 8, with a score of: 0.230656224605412
WordScorePoS
goal0NN
stakeholder0NN
issue0NN
enhance0.0123650647908625VB
welfare0NN
economic0.128463821595706JJ
contribution0NN
urbanization0NN
sustainable0.00688808874408283JJ
urban0JJ
development0.0055149369084567NN

Label: 17, with a score of: 0.33491417325766
WordScorePoS
goal0NN
stakeholder0.0739231123597559NN
issue0.0299586785922821NN
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welfare0NN
economic0JJ
contribution0NN
urbanization0NN
sustainable0.0115176738164035JJ
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development0.0469463607292177NN

Based on these consultations seven critical urban challenges emerged

Label: 11, with a score of: 0.926659955858337
WordScorePoS
base0NN
critical0JJ
urban0.27622591898666JJ
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Table summarizes these challenges and the associated needs

Label: 11, with a score of: 0.799666530083443
WordScorePoS
challenge0.0947742174515357NN

Label: 7, with a score of: 0.446136416745782
WordScorePoS
challenge0.0528748274225028NN

Needs Tailoring research and best practices to the particular context and needs of different types of cities City level data to develop typology of cities Harnessing opportunities to transition to knowledge economies Enabling environment for knowledge creation and entrepreneurship Upgrading well being for citizens businesses investors and visitors Plans to fill fundamental infrastructure and service deficits with the corresponding financing and delivery mechanisms Developing sustainable smart cities Mainstreaming of sustainability issues in the planning design and construction communities and innovation at all stages of development Fostering civic renewal citizen trust and public confidence in city leadership Two way communication with government institutional strengthening and collaboration with civil society Redefining the city relationship with supra national bodies in view of the absence of binding international agreements on climate change sustainability and green growth Establishment of new format based on partnerships among cities the private sector academia and civil society Enabling city learning and peer to peer sharing for city leaders Open data standardized city indicators benchmarking of cities and municipal networks to facilitate the exchange of knowledge TABLE Key Challenges for Sustainable Urban Development BUILDING SUSTAINABILITY IN AN URBANIZING WORLD articulate their own sustainable development goals and encourage their national governments to aggregate these

Label: 11, with a score of: 0.890524737342123
WordScorePoS
research0NN
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type0NN
city0.607697021770652NN
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knowledge0NN
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enable0.0373517915079663VB
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sustainable0.00782503464617421JJ
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community0NN
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change0.0315914058171786NN
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challenge0.0947742174515357NN
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building0.0747035830159326NN
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goal0NN
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Label: 17, with a score of: 0.282899132461959
WordScorePoS
research0NN
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sustainable0.0115176738164035JJ
sustainability0.036961556179878NN
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innovation0.0499801157438617NN
stage0NN
development0.0469463607292177NN
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agreement0NN
climate0NN
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green0JJ
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base0.017987180284335NN
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learn0VB
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municipal0JJ
facilitate0.0211360962287062VB
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challenge0NN
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building0.149940347231585NN
world0.00418824502414673NN
goal0NN
encourage0.0211360962287062VB

Label: 9, with a score of: 0.151648124882173
WordScorePoS
research0.104016094894625NN
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datum0.0454743001434393NN
develop0VB
opportunity0.0135141354781442NN
knowledge0NN
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enable0VB
environment0.0221298700466866NN
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entrepreneurship0NN
upgrade0.0520080474473126NN
business0.0909486002868787NN
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fundamental0JJ
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sustainable0.00901749491165909JJ
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community0.0188543197850406NN
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international0.00265959362690358JJ
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climate0NN
change0NN
green0JJ
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learn0VB
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municipal0JJ
facilitate0.0260040237236563VB
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urban0JJ
building0NN
world0.00257642711761688NN
goal0NN
encourage0.0260040237236563VB

Label: 13, with a score of: 0.147033841990417
WordScorePoS
research0NN
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context0.0414107553022249NN
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city0NN
level0.0415277415054218NN
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economy0.0839946104936743NN
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environment0NN
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climate0.291881734282273NN
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green0.0335649154577099JJ
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urban0JJ
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world0.00351929966767313NN
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Label: 8, with a score of: 0.124188560475444
WordScorePoS
research0NN
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city0NN
level0.00761994916892276NN
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develop0VB
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entrepreneurship0.0607878664258118NN
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sustainable0.00688808874408283JJ
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municipal0JJ
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challenge0.0347609334428716NN
urban0JJ
building0NN
world0.00172202218602071NN
goal0NN
encourage0.0695218668857431VB

Label: 4, with a score of: 0.0771417277167825
WordScorePoS
research0NN
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level0.0231525547935232NN
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Useful models for urban sustainability planning include Local Agenda the Urban Environmental Accords City Development Strategies Green Plans and Sustainability Master Plans

Label: 11, with a score of: 0.940345928772648
WordScorePoS
urban0.27622591898666JJ
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green0.04477822029736JJ

The planning process should involve considering sustainability within traditional cost benefit analysis garnering public support and identifying sources of finance that can make these long term plans affordable for the city budget

Label: 11, with a score of: 0.818564751427639
WordScorePoS
plan0.0583751976558017NN
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involve0VB
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public0.0583751976558017NN
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city0.607697021770652NN

Label: 17, with a score of: 0.313016118337072
WordScorePoS
plan0.0130185595639838NN
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cost0NN
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To develop sustainably cities need credible voice in global policies finance to speed implementation and access to information

Label: 11, with a score of: 0.858284514583729
WordScorePoS
develop0VB
sustainably0RB
city0.607697021770652NN
credible0JJ
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global0.00501208088147356JJ
policy0.0136980135278128NN
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access0.00626002771693937NN
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Label: 10, with a score of: 0.375057706160014
WordScorePoS
develop0VB
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credible0.064698038783335JJ
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The Sustainable Cities Partnership has identified specific areas for cities and their partners to focus on ' Data collection Cities and their agencies should collect relevant and credible information and apply practical tools to help improve decision making for cities

Label: 11, with a score of: 0.95902187820767
WordScorePoS
sustainable0.00782503464617421JJ
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relevant0JJ
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information0NN
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improve0.0273960270556257VB
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Label: 17, with a score of: 0.234553821681786
WordScorePoS
sustainable0.0115176738164035JJ
city0NN
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The data needs to provide composite picture of their environmental impacts and overall consumption emissions and outputs that is their urban metabolism

Label: 12, with a score of: 0.668305299790407
WordScorePoS
datum0NN
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emission0.0554128417527413NN
urban0JJ

Label: 11, with a score of: 0.607186713275789
WordScorePoS
datum0NN
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urban0.27622591898666JJ

Label: 13, with a score of: 0.28920930223912
WordScorePoS
datum0NN
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Label: 8, with a score of: 0.213995105747862
WordScorePoS
datum0NN
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environmental0.0295821503613099JJ
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This will allow diagnostics benchmarking and relevant indicators to be established for cities which in turn enable cities to track their service delivery and well being over time and compared to other cities

Label: 11, with a score of: 0.989095086893232
WordScorePoS
relevant0JJ
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Such comparison are particularly useful for cities to learn from one another and form networks around common objectives

Label: 11, with a score of: 0.881735819751189
WordScorePoS
city0.607697021770652NN
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Label: 4, with a score of: 0.325221044447745
WordScorePoS
city0NN
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Label: 16, with a score of: 0.210210770927887
WordScorePoS
city0NN
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' Assessing paths to sustainability at sector level Cities and their agencies should gather data with special focus on buildings retrofitting reuse new green building codes energy efficiency sources of loss potential efficiency gains transport fleet composition capture of land value increases role of personal vehicles type of fuel electrification policies land use planning and density management and basic service provision water wastewater solid waste drainage

Label: 11, with a score of: 0.578775105797903
WordScorePoS
sustainability0NN
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loss0.0315914058171786NN
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gain0.04477822029736NN
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land0.0389167984372011NN
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personal0JJ
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policy0.0136980135278128NN
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water0.0458109463416786NN
wastewater0NN
waste0.0373517915079663NN

Label: 12, with a score of: 0.447300263651231
WordScorePoS
sustainability0.0409791940864438NN
sector0.0169905820958638NN
level0.00513687014008325NN
city0NN
agency0.0542519672965169NN
datum0NN
special0JJ
focus0NN
building0NN
reuse0.0409791940864438NN
green0.0332151194779746JJ
energy0.133960988253756NN
source0NN
loss0.0468670845659596NN
potential0NN
gain0.0664302389559493NN
transport0.0277064208763707NN
capture0NN
land0.0144336476662975NN
increase0.00958677785775002NN
personal0.0542519672965169JJ
vehicle0.217007869186068NN
type0NN
fuel0.0332151194779746NN
policy0.0203215381458133NN
plan0.0144336476662975NN
density0NN
management0.0339811641917276NN
basic0.0144336476662975JJ
service0.00666957305782837NN
provision0NN
water0.186896403054502NN
wastewater0NN
waste0.138532104381853NN

Label: 6, with a score of: 0.436144025585382
WordScorePoS
sustainability0NN
sector0.0214003646985842NN
level0.00647010760354177NN
city0NN
agency0NN
datum0NN
special0.0348974218717993JJ
focus0NN
building0.0348974218717993NN
reuse0.103230094596636NN
green0JJ
energy0.0153390539205676NN
source0.0613562156822703NN
loss0NN
potential0NN
gain0.041835863322702NN
transport0NN
capture0NN
land0NN
increase0.00905621749519576NN
personal0JJ
vehicle0NN
type0NN
fuel0NN
policy0NN
plan0NN
density0NN
management0.0428007293971684NN
basic0.0181797964452808JJ
service0.0084006124696649NN
provision0NN
water0.556409482163189NN
wastewater0.204998018174509NN
waste0NN

Label: 7, with a score of: 0.28709256928946
WordScorePoS
sustainability0NN
sector0NN
level0NN
city0NN
agency0NN
datum0NN
special0JJ
focus0NN
building0NN
reuse0NN
green0JJ
energy0.494617163780726NN
source0.0274787313211514NN
loss0NN
potential0NN
gain0NN
transport0NN
capture0NN
land0.0325677023224526NN
increase0.0162235147372888NN
personal0JJ
vehicle0NN
type0NN
fuel0.0749457204978913NN
policy0NN
plan0NN
density0NN
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basic0JJ
service0.0451471467893167NN
provision0NN
water0NN
wastewater0NN
waste0.0625160381065121NN

Label: 17, with a score of: 0.218805380072609
WordScorePoS
sustainability0.036961556179878NN
sector0.0459744293658165NN
level0.0138997399903163NN
city0NN
agency0NN
datum0.0739231123597559NN
special0JJ
focus0NN
building0.149940347231585NN
reuse0NN
green0JJ
energy0.0109843026967392NN
source0.0109843026967392NN
loss0NN
potential0NN
gain0NN
transport0.0249900578719309NN
capture0NN
land0NN
increase0.00432344066632653NN
personal0JJ
vehicle0NN
type0NN
fuel0NN
policy0.0641521854453136NN
plan0.0130185595639838NN
density0NN
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basic0JJ
service0NN
provision0NN
water0NN
wastewater0NN
waste0NN

' Costing the alternatives and acquiring stable finance Cities should encourage the use of frameworks such as natural capital accounting and ensure that these are applicable at the local level

Label: 11, with a score of: 0.823085378941532
WordScorePoS
cost0NN
alternative0NN
acquire0NN
stable0JJ
city0.607697021770652NN
encourage0VB
framework0.0194583992186006NN
natural0.0229054731708393JJ
accounting0NN
ensure0.00501208088147356VB
applicable0JJ
local0.0194583992186006JJ
level0.00692515656684925NN

' Discussing and articulating local sustainability goals Cities should mainstream sustainability planning into multi year development planning processes

Label: 11, with a score of: 0.896096745578028
WordScorePoS
local0.0194583992186006JJ
sustainability0NN
goal0NN
city0.607697021770652NN
plan0.0583751976558017NN
multus0NN
development0.0100241617629471NN
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Label: 17, with a score of: 0.306402340434425
WordScorePoS
local0.0130185595639838JJ
sustainability0.036961556179878NN
goal0NN
city0NN
plan0.0130185595639838NN
multus0.0978661089756501NN
development0.0469463607292177NN
process0NN

They should ' Sharing knowledge among institutions Cities institutions and partners should work toward the use of common tools to guide monitor and communicate progress

Label: 11, with a score of: 0.682466981178801
WordScorePoS
share0NN
knowledge0NN
institution0NN
city0.607697021770652NN
common0.055245183797332JJ
tool0NN
monitor0NN
progress0NN

Label: 16, with a score of: 0.565122121968447
WordScorePoS
share0NN
knowledge0NN
institution0.274477239401321NN
city0NN
common0JJ
tool0NN
monitor0NN
progress0NN

These include the Global Protocol for CommunityScale GHG Emissions

Label: 13, with a score of: 0.870432500497443
WordScorePoS
include0VB
global0.0375696197697663JJ
emission0.19598742448524NN

Label: 7, with a score of: 0.326779076014135
WordScorePoS
include0VB
global0.0251662980592747JJ
emission0.0625160381065121NN

the GCIF and UNEP Knowledge Centre on Cities and Climate Change

Label: 13, with a score of: 0.677153232277476
WordScorePoS
knowledge0NN
centre0NN
city0NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 11, with a score of: 0.645703069024965
WordScorePoS
knowledge0NN
centre0NN
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN

The use of information nodes by institutions at the city regional national and international levels will ensure that credible information on sustainable cities is widely available and regularly updated

Label: 11, with a score of: 0.895511765505747
WordScorePoS
information0NN
institution0NN
city0.607697021770652NN
regional0.0315914058171786JJ
national0.00501208088147356JJ
international0JJ
level0.00692515656684925NN
ensure0.00501208088147356VB
credible0JJ
sustainable0.00782503464617421JJ
widely0RB

Label: 16, with a score of: 0.326416905056145
WordScorePoS
information0.0241291244414115NN
institution0.274477239401321NN
city0NN
regional0JJ
national0.0294647633960925JJ
international0.0189945307832458JJ
level0.0610668815653026NN
ensure0.0220985725470694VB
credible0JJ
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widely0RB

' Collaborating with partner cities in other parts of the world Cities should partner to share important lessons facilitate innovation and help to spread new sustainable cites paradigm

Label: 11, with a score of: 0.963552622149605
WordScorePoS
city0.607697021770652NN
world0.00626002771693937NN
share0NN
facilitate0VB
innovation0.0373517915079663NN
spread0NN
sustainable0.00782503464617421JJ

Next Steps for the Sustainable Cities Partnership The Sustainable Cities Partnership is an evolving network rather than formal body with uniform agenda

Label: 11, with a score of: 0.809687408383231
WordScorePoS
sustainable0.00782503464617421JJ
city0.607697021770652NN
partnership0NN
agenda0NN

Label: 17, with a score of: 0.578723122855985
WordScorePoS
sustainable0.0115176738164035JJ
city0NN
partnership0.3914644359026NN
agenda0.0739231123597559NN

The group as whole has agreed on broad aspirations while individual members are pursuing specific programs that are aligned with their own core objectives as well as the mission of the wider partnership

Label: 17, with a score of: 0.953890455056488
WordScorePoS
agree0.0422721924574123VB
individual0JJ
pursue0VB
objective0.0299586785922821NN
partnership0.3914644359026NN

ghgprotocol

Label: 0, with a score of: 1

org city accounting http www

Label: 11, with a score of: 0.983417014813576
WordScorePoS
city0.607697021770652NN
accounting0NN

citiesandclimatechange

Label: 0, with a score of: 1

org PART IV THE PATH FORWARD Going forward our general goals are to ' Develop common lexicon of terms tools and metrics for sustainable cities

Label: 11, with a score of: 0.951941163957241
WordScorePoS
goal0NN
develop0VB
common0.055245183797332JJ
term0NN
tool0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN

Label: 17, with a score of: 0.228929851461782
WordScorePoS
goal0NN
develop0VB
common0JJ
term0.149793392961411NN
tool0NN
sustainable0.0115176738164035JJ
city0NN

' In the public sector identify the benefits derived from well functioning public institutions discuss policy alternatives including carbon tax and fuel subsidies and have open dialogue with constituents regarding the legitimate issues faced by cities and the urgency of moving forward

Label: 11, with a score of: 0.673153257583421
WordScorePoS
public0.0583751976558017NN
sector0NN
identify0VB
function0NN
institution0NN
policy0.0136980135278128NN
alternative0NN
include0VB
carbon0.0373517915079663NN
tax0NN
fuel0NN
subsidy0NN
legitimate0JJ
issue0NN
face0NN
city0.607697021770652NN
move0NN

Label: 16, with a score of: 0.375854092612505
WordScorePoS
public0.0285977592242194NN
sector0NN
identify0VB
function0NN
institution0.274477239401321NN
policy0.0201317944152378NN
alternative0NN
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carbon0NN
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fuel0NN
subsidy0NN
legitimate0JJ
issue0NN
face0NN
city0NN
move0NN

Label: 14, with a score of: 0.211118970077544
WordScorePoS
public0NN
sector0NN
identify0.0531983120572613VB
function0NN
institution0NN
policy0NN
alternative0NN
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carbon0.0271683202880942NN
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subsidy0.162850158165781NN
legitimate0JJ
issue0NN
face0NN
city0NN
move0NN

' Highlight the role of the private sector and mobilize businesses to help innovate investment products processes and institutions for sustainable urban development while ensuring that the needs of cities and their residents are met

Label: 11, with a score of: 0.760461735087387
WordScorePoS
private0JJ
sector0NN
mobilize0VB
business0NN
investment0NN
product0.0268848280079943NN
process0NN
institution0NN
sustainable0.00782503464617421JJ
urban0.27622591898666JJ
development0.0100241617629471NN
ensure0.00501208088147356VB
city0.607697021770652NN
meet0VB

Label: 16, with a score of: 0.279049146622538
WordScorePoS
private0JJ
sector0NN
mobilize0VB
business0NN
investment0NN
product0NN
process0NN
institution0.274477239401321NN
sustainable0.00920028227269801JJ
urban0JJ
development0.0368309542451156NN
ensure0.0220985725470694VB
city0NN
meet0VB

Label: 17, with a score of: 0.331614608384235
WordScorePoS
private0.0898760357768464JJ
sector0.0459744293658165NN
mobilize0.0898760357768464VB
business0NN
investment0.0766240489430275NN
product0.017987180284335NN
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institution0.0249900578719309NN
sustainable0.0115176738164035JJ
urban0JJ
development0.0469463607292177NN
ensure0.00335331148065841VB
city0NN
meet0VB

Label: 9, with a score of: 0.314558112957745
WordScorePoS
private0.0368585655748969JJ
sector0.0565629593551219NN
mobilize0VB
business0.0909486002868787NN
investment0.0377086395700813NN
product0.0442597400933733NN
process0.0737171311497939NN
institution0NN
sustainable0.00901749491165909JJ
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development0.0330049940546426NN
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' Develop decision making framework based on simple qualitative multi dimensional analysis that captures the various impacts of green growth policies

Label: 8, with a score of: 0.49676574708312
WordScorePoS
develop0VB
decision0NN
framework0.0428212738652355NN
base0NN
simple0JJ
multus0NN
capture0NN
impact0NN
green0JJ
growth0.208565600657229NN
policy0.0452169560888236NN

Label: 17, with a score of: 0.440640575418722
WordScorePoS
develop0VB
decision0.0211360962287062NN
framework0.0130185595639838NN
base0.017987180284335NN
simple0.048933054487825JJ
multus0.0978661089756501NN
capture0NN
impact0NN
green0JJ
growth0NN
policy0.0641521854453136NN

Label: 1, with a score of: 0.377090542800631
WordScorePoS
develop0VB
decision0.0519476926891779NN
framework0.0319966432858631NN
base0.0442083771593746NN
simple0JJ
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capture0NN
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growth0.0519476926891779NN
policy0.0450489732120807NN

Label: 10, with a score of: 0.376084041907152
WordScorePoS
develop0VB
decision0.02794560829783NN
framework0NN
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simple0JJ
multus0NN
capture0NN
impact0NN
green0JJ
growth0.11178243319132NN
policy0.0848203862485078NN

Label: 12, with a score of: 0.316459117194789
WordScorePoS
develop0VB
decision0NN
framework0.028867295332595NN
base0.0199423462679015NN
simple0JJ
multus0NN
capture0NN
impact0.0866018859977851NN
green0.0332151194779746JJ
growth0NN
policy0.0203215381458133NN

More specifically the partnership can contribute to variety of urban sustainability objectives Table and several concrete commitments have been made

Label: 17, with a score of: 0.850616995577769
WordScorePoS
partnership0.3914644359026NN
contribute0.0249900578719309VB
variety0NN
urban0JJ
sustainability0.036961556179878NN
objective0.0299586785922821NN
commitment0.0739231123597559NN

Label: 11, with a score of: 0.421610212820803
WordScorePoS
partnership0NN
contribute0VB
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urban0.27622591898666JJ
sustainability0NN
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Partners plan to collectively develop some key building blocks of sustainable cities working along the following lines of action ' The World Bank and other partners will periodically compile and disseminate the Large Urban Areas Compendium introduced in Chapter

Label: 11, with a score of: 0.933028131062136
WordScorePoS
plan0.0583751976558017NN
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key0NN
building0.0747035830159326NN
sustainable0.00782503464617421JJ
city0.607697021770652NN
line0.0373517915079663NN
action0NN
world0.00626002771693937NN
bank0NN
urban0.27622591898666JJ
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Label: 17, with a score of: 0.197890780375687
WordScorePoS
plan0.0130185595639838NN
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key0NN
building0.149940347231585NN
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city0NN
line0NN
action0.0109843026967392NN
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bank0.036961556179878NN
urban0JJ
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Partners also plan to provide available city data to the GCIF for standardized data monitoring and to regularly update information relevant for cities at different levels of development emissions and technological capacity

Label: 11, with a score of: 0.88217006425125
WordScorePoS
plan0.0583751976558017NN
provide0.0269743071558856VB
city0.607697021770652NN
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information0NN
relevant0JJ
level0.00692515656684925NN
development0.0100241617629471NN
emission0.0373517915079663NN
technological0.0315914058171786JJ
capacity0.0112376090361851NN

Label: 13, with a score of: 0.214136554342388
WordScorePoS
plan0.0291713033871158NN
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city0NN
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relevant0JJ
level0.0415277415054218NN
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emission0.19598742448524NN
technological0.0473606524958885JJ
capacity0.025270503924109NN

' Partners will provide coordinated input to other key products such as standardized risk evaluations using the Urban Risk Assessment methodology Box

Label: 11, with a score of: 0.805024883838081
WordScorePoS
provide0.0269743071558856VB
coordinate0NN
input0NN
key0NN
product0.0268848280079943NN
risk0.0631828116343571NN
urban0.27622591898666JJ

' The Institute for Sustainable Infrastructure ISI is taking the lead to develop an infrastructure rating tool

Label: 9, with a score of: 0.845170857031705
WordScorePoS
sustainable0.00901749491165909JJ
infrastructure0.192202909044225NN
lead0.0188543197850406NN
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tool0NN

Label: 7, with a score of: 0.367125766070214
WordScorePoS
sustainable0.0104774661535722JJ
infrastructure0.0651354046449052NN
lead0.0383371018038178NN
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As part of memorandum of understanding on climate change and common urban metrics signed by all IFIs at meetings of the IPCC in December and the infrastructure rating tool will provide platform to assess the sustainability of infrastructure projects

Label: 13, with a score of: 0.703158543593698
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
common0JJ
urban0JJ
december0.0548233071065585NNP
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platform0NN
sustainability0NN
project0.0335649154577099NN

Label: 11, with a score of: 0.405868891406449
WordScorePoS
climate0.0229054731708393NN
change0.0315914058171786NN
common0.055245183797332JJ
urban0.27622591898666JJ
december0NNP
infrastructure0.0194583992186006NN
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Label: 9, with a score of: 0.351928895935161
WordScorePoS
climate0NN
change0NN
common0JJ
urban0JJ
december0NNP
infrastructure0.192202909044225NN
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provide0.00740117451847634VB
platform0NN
sustainability0NN
project0NN

Label: 7, with a score of: 0.315303773096572
WordScorePoS
climate0.115011305411453NN
change0.105749654845006NN
common0JJ
urban0JJ
december0NNP
infrastructure0.0651354046449052NN
tool0NN
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platform0NN
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Those projects with particularly high sustainability rating may be eligible for preferential funding such as green bonds

Label: 12, with a score of: 0.654134859691696
WordScorePoS
project0.0664302389559493NN
sustainability0.0409791940864438NN
preferential0JJ
green0.0332151194779746JJ

Label: 17, with a score of: 0.627169823820746
WordScorePoS
project0NN
sustainability0.036961556179878NN
preferential0.0978661089756501JJ
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As of June draft of the tool exists and is being piloted by several engineering firms

Label: 12, with a score of: 0.620208386859689
WordScorePoS
tool0.0542519672965169NN
exist0.0332151194779746VB
engineering0NN

Label: 4, with a score of: 0.577946373778109
WordScorePoS
tool0NN
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' Annex presents the South Africa Cities Accord that emerged during the Durban COP

Label: 11, with a score of: 0.995158755405856
WordScorePoS
south0RB
africa0IN
city0.607697021770652NN
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The Accord recognizes that urban data collection should first rest with respective cities

Label: 11, with a score of: 0.97034575344724
WordScorePoS
recognize0VB
urban0.27622591898666JJ
datum0NN
collection0NN
respective0JJ
city0.607697021770652NN

Annual and open publication is best

Label: 0, with a score of: 1

Where practical city indicators should have globally recognized format or standard

Label: 11, with a score of: 0.980830080144555
WordScorePoS
city0.607697021770652NN
globally0RB
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standard0NN

' The USGBC in partnership with the World Green Buildings Council is leading the development of common guideline for green buildings

Label: 17, with a score of: 0.850077038097943
WordScorePoS
partnership0.3914644359026NN
world0.00418824502414673NN
green0JJ
building0.149940347231585NN
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Label: 11, with a score of: 0.355488425968946
WordScorePoS
partnership0NN
world0.00626002771693937NN
green0.04477822029736JJ
building0.0747035830159326NN
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development0.0100241617629471NN
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As shared guidelines emerge cities are expected to support their widespread application as are agencies such as the World Bank and members of the Sustainable Cities Partnership

Label: 11, with a score of: 0.884720415164216
WordScorePoS
share0NN
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city0.607697021770652NN
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world0.00626002771693937NN
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Label: 17, with a score of: 0.399881934885346
WordScorePoS
share0.0766240489430275NN
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world0.00418824502414673NN
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sustainable0.0115176738164035JJ
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' City agencies like and ICLEI as well as national city associations national governments and international organizations are working with cities to develop new strategies for coordinating sustainable development meshing urban climate change mitigation adaptation and basic service delivery

Label: 11, with a score of: 0.900682825707762
WordScorePoS
city0.607697021770652NN
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national0.00501208088147356JJ
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climate0.0229054731708393NN
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adaptation0.04477822029736NN
basic0.0583751976558017JJ
service0.0269743071558856NN

Label: 13, with a score of: 0.356066726612766
WordScorePoS
city0NN
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national0.0112708859309299JJ
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climate0.291881734282273NN
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An example is the Climate and Clean Air Coalition CCAC broad and growing partnership that aims to reduce short lived climate pollutants such as methane black carbon tropospheric ozone and hydrofluorocarbons

Label: 13, with a score of: 0.680983094173299
WordScorePoS
climate0.291881734282273NN
clean0JJ
air0NN
grow0.0171695137813101VB
partnership0NN
aim0NN
reduce0.00375696197697663VB
live0VB
carbon0.0559964069957828NN

Label: 7, with a score of: 0.439045262105367
WordScorePoS
climate0.115011305411453NN
clean0.125032076213024JJ
air0NN
grow0VB
partnership0NN
aim0NN
reduce0.00838876601975825VB
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carbon0.0625160381065121NN

Label: 17, with a score of: 0.432704592141506
WordScorePoS
climate0NN
clean0JJ
air0NN
grow0VB
partnership0.3914644359026NN
aim0.0249900578719309NN
reduce0.00335331148065841VB
live0VB
carbon0NN

CCAC includes an initiative on solid waste management

Label: 12, with a score of: 0.632318429203992
WordScorePoS
include0VB
initiative0NN
waste0.138532104381853NN
management0.0339811641917276NN

Label: 7, with a score of: 0.568054686353505
WordScorePoS
include0VB
initiative0.0924643738905717NN
waste0.0625160381065121NN
management0NN

Label: 11, with a score of: 0.38877522370022
WordScorePoS
include0VB
initiative0NN
waste0.0373517915079663NN
management0.0687164195125178NN

BUILDING SUSTAINABILITY IN AN URBANIZING WORLD TABLE Moving Forward in the Sustainable Cities Partnership City Objective Tools Existing Sources Role of the Partnership Understanding the city its ecological impacts and the structure of its emissions Indicators and data collection ' greenhouse gas inventory ' sustainability ' ecosystem services Rapid sustainability assessment GCIF ICLEI and UNEP City planning Promotion and support Use of common tools Comparison with relevant cities Benchmarks according to income density production and geographic groups Green indexes Common and regular metrics Developing more operational definition of sustainable city Training Assessing alternative paths to Energy efficiency sustainability Building codes Building retrofitting Transport policies Land development Water wastewater and solid waste ESMAP UNEP Local and national master plans Remote sensing Developing toolkits Associating city typology with interventions Costing alternatives Broader cost benefit analyses Private sector MDBs cities Promoting internalization of more externalities Including sustainability goals in capital improvement plans Infrastructure sustainability ratings Local national agreements Local citizen support ASCE WFEO and ISO infrastructure rating tool Financing Climate finance political autonomy Local and national revenues MDB partnership private sector UrbKP Expand and link UrbKP Implementation and follow up Reports seminars internet information warehousing ' The WBCSD will work with the Sustainable Cities Partnership to develop an approach for cities to create smart city platforms

Label: 11, with a score of: 0.833301397881394
WordScorePoS
building0.0747035830159326NN
sustainability0NN
world0.00626002771693937NN
move0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN
partnership0NN
objective0NN
tool0NN
exist0.04477822029736VB
source0NN
impact0.0194583992186006NN
structure0NN
emission0.0373517915079663NN
datum0NN
collection0NN
greenhouse0NN
gas0NN
ecosystem0NN
service0.0269743071558856NN
rapid0.0373517915079663JJ
plan0.0583751976558017NN
promotion0NN
support0.0224752180723703NN
common0.055245183797332JJ
relevant0JJ
income0NN
density0.0731385760866977NN
production0NN
geographic0JJ
green0.04477822029736JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.0492535935240667NN
transport0.0747035830159326NN
policy0.0136980135278128NN
land0.0389167984372011NN
development0.0100241617629471NN
water0.0458109463416786NN
wastewater0NN
waste0.0373517915079663NN
local0.0194583992186006JJ
national0.00501208088147356JJ
sense0NN
intervention0NN
cost0NN
private0JJ
sector0NN
promote0VB
include0VB
goal0NN
improvement0NN
infrastructure0.0194583992186006NN
agreement0NN
financing0NN
climate0.0229054731708393NN
political0JJ
autonomy0NN
revenue0NN
expand0.0268848280079943VB
implementation0NN
report0NN
internet0NN
information0NN
approach0NN
create0.0315914058171786VB
platform0NN

Label: 17, with a score of: 0.372370463681199
WordScorePoS
building0.149940347231585NN
sustainability0.036961556179878NN
world0.00418824502414673NN
move0NN
sustainable0.0115176738164035JJ
city0NN
partnership0.3914644359026NN
objective0.0299586785922821NN
tool0NN
exist0.0599173571845643VB
source0.0109843026967392NN
impact0NN
structure0.048933054487825NN
emission0NN
datum0.0739231123597559NN
collection0.048933054487825NN
greenhouse0NN
gas0NN
ecosystem0NN
service0NN
rapid0JJ
plan0.0130185595639838NN
promotion0.0249900578719309NN
support0.0451108483130998NN
common0JJ
relevant0.0249900578719309JJ
income0.017987180284335NN
density0NN
production0NN
geographic0.048933054487825JJ
green0JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.0109843026967392NN
transport0.0249900578719309NN
policy0.0641521854453136NN
land0NN
development0.0469463607292177NN
water0NN
wastewater0NN
waste0NN
local0.0130185595639838JJ
national0.0134132459226336JJ
sense0NN
intervention0NN
cost0NN
private0.0898760357768464JJ
sector0.0459744293658165NN
promote0.0100599344419752VB
include0VB
goal0NN
improvement0NN
infrastructure0.0130185595639838NN
agreement0NN
financing0.036961556179878NN
climate0NN
political0JJ
autonomy0NN
revenue0.0978661089756501NN
expand0VB
implementation0.017987180284335NN
report0NN
internet0.0739231123597559NN
information0.0219686053934784NN
approach0NN
create0VB
platform0NN

Label: 12, with a score of: 0.204901575170183
WordScorePoS
building0NN
sustainability0.0409791940864438NN
world0.00464349782489754NN
move0.0409791940864438NN
sustainable0.0162522423871414JJ
city0NN
partnership0NN
objective0NN
tool0.0542519672965169NN
exist0.0332151194779746VB
source0NN
impact0.0866018859977851NN
structure0NN
emission0.0554128417527413NN
datum0NN
collection0NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
ecosystem0.0234335422829798NN
service0.00666957305782837NN
rapid0JJ
plan0.0144336476662975NN
promotion0NN
support0.00833571598658786NN
common0JJ
relevant0.0277064208763707JJ
income0NN
density0NN
production0.118934074671047NN
geographic0JJ
green0.0332151194779746JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.133960988253756NN
transport0.0277064208763707NN
policy0.0203215381458133NN
land0.0144336476662975NN
development0.022306853314744NN
water0.186896403054502NN
wastewater0NN
waste0.138532104381853NN
local0.0144336476662975JJ
national0.00743561777158133JJ
sense0NN
intervention0NN
cost0.0277064208763707NN
private0JJ
sector0.0169905820958638NN
promote0.0148712355431627VB
include0VB
goal0NN
improvement0NN
infrastructure0.028867295332595NN
agreement0NN
financing0NN
climate0NN
political0JJ
autonomy0NN
revenue0NN
expand0VB
implementation0.0199423462679015NN
report0.0409791940864438NN
internet0NN
information0.0365348149782971NN
approach0.0542519672965169NN
create0.0234335422829798VB
platform0NN

Label: 9, with a score of: 0.158580422122387
WordScorePoS
building0NN
sustainability0NN
world0.00257642711761688NN
move0NN
sustainable0.00901749491165909JJ
city0NN
partnership0NN
objective0.0368585655748969NN
tool0NN
exist0VB
source0.0135141354781442NN
impact0.0160169090870187NN
structure0NN
emission0NN
datum0.0454743001434393NN
collection0NN
greenhouse0NN
gas0NN
ecosystem0NN
service0.0148023490369527NN
rapid0.030745604615229JJ
plan0NN
promotion0NN
support0.0277502419802315NN
common0JJ
relevant0JJ
income0.110649350233433NN
density0NN
production0.0188543197850406NN
geographic0JJ
green0JJ
regular0JJ
develop0VB
training0.030745604615229NN
alternative0.0602029956716497NN
energy0.0540565419125769NN
transport0.030745604615229NN
policy0.011275328195446NN
land0NN
development0.0330049940546426NN
water0.0377086395700813NN
wastewater0NN
waste0NN
local0JJ
national0.00412562425683033JJ
sense0NN
intervention0NN
cost0NN
private0.0368585655748969JJ
sector0.0565629593551219NN
promote0.00825124851366066VB
include0VB
goal0NN
improvement0.0454743001434393NN
infrastructure0.192202909044225NN
agreement0NN
financing0NN
climate0NN
political0.0368585655748969JJ
autonomy0NN
revenue0NN
expand0VB
implementation0NN
report0NN
internet0.0454743001434393NN
information0.0675706773907211NN
approach0NN
create0.0520080474473126VB
platform0NN

Label: 7, with a score of: 0.155113310184391
WordScorePoS
building0NN
sustainability0NN
world0.00261936653839306NN
move0NN
sustainable0.0104774661535722JJ
city0NN
partnership0NN
objective0.0749457204978913NN
tool0NN
exist0VB
source0.0274787313211514NN
impact0NN
structure0NN
emission0.0625160381065121NN
datum0NN
collection0NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
ecosystem0NN
service0.0451471467893167NN
rapid0JJ
plan0NN
promotion0NN
support0.0188084899376888NN
common0JJ
relevant0JJ
income0.0449973847138318NN
density0NN
production0.0383371018038178NN
geographic0JJ
green0JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.494617163780726NN
transport0NN
policy0NN
land0.0325677023224526NN
development0NN
water0NN
wastewater0NN
waste0.0625160381065121NN
local0JJ
national0JJ
sense0NN
intervention0NN
cost0NN
private0JJ
sector0NN
promote0.00838876601975825VB
include0VB
goal0NN
improvement0.0924643738905717NN
infrastructure0.0651354046449052NN
agreement0NN
financing0NN
climate0.115011305411453NN
political0JJ
autonomy0NN
revenue0NN
expand0.0449973847138318VB
implementation0NN
report0NN
internet0NN
information0NN
approach0NN
create0VB
platform0NN

Label: 6, with a score of: 0.129421376180282
WordScorePoS
building0.0348974218717993NN
sustainability0NN
world0.00438651305628668NN
move0NN
sustainable0.00146217101876223JJ
city0NN
partnership0NN
objective0NN
tool0NN
exist0VB
source0.0613562156822703NN
impact0.0181797964452808NN
structure0NN
emission0NN
datum0NN
collection0NN
greenhouse0NN
gas0NN
ecosystem0.0295155485671997NN
service0.0084006124696649NN
rapid0JJ
plan0NN
promotion0NN
support0.0209983814716066NN
common0JJ
relevant0JJ
income0NN
density0NN
production0.0214003646985842NN
geographic0JJ
green0JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.0153390539205676NN
transport0NN
policy0NN
land0NN
development0NN
water0.556409482163189NN
wastewater0.204998018174509NN
waste0NN
local0.0181797964452808JJ
national0JJ
sense0NN
intervention0NN
cost0NN
private0JJ
sector0.0214003646985842NN
promote0VB
include0VB
goal0NN
improvement0NN
infrastructure0.0181797964452808NN
agreement0NN
financing0NN
climate0NN
political0JJ
autonomy0NN
revenue0NN
expand0.0251182378961834VB
implementation0NN
report0NN
internet0NN
information0NN
approach0NN
create0VB
platform0NN

Label: 13, with a score of: 0.112270770354049
WordScorePoS
building0NN
sustainability0NN
world0.00351929966767313NN
move0.0414107553022249NN
sustainable0JJ
city0NN
partnership0NN
objective0NN
tool0NN
exist0VB
source0.0123065238088614NN
impact0.0437569550806737NN
structure0NN
emission0.19598742448524NN
datum0NN
collection0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
ecosystem0NN
service0NN
rapid0JJ
plan0.0291713033871158NN
promotion0NN
support0NN
common0JJ
relevant0JJ
income0NN
density0NN
production0NN
geographic0JJ
green0.0335649154577099JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.0123065238088614NN
transport0NN
policy0.0102677744436108NN
land0NN
development0NN
water0NN
wastewater0NN
waste0NN
local0.0145856516935579JJ
national0.0112708859309299JJ
sense0NN
intervention0NN
cost0.0279982034978914NN
private0JJ
sector0NN
promote0.00375696197697663VB
include0VB
goal0NN
improvement0NN
infrastructure0NN
agreement0.0236803262479443NN
financing0NN
climate0.291881734282273NN
political0JJ
autonomy0NN
revenue0NN
expand0.0201523636533764VB
implementation0.0201523636533764NN
report0NN
internet0NN
information0NN
approach0NN
create0VB
platform0NN

Label: 10, with a score of: 0.0869969726230956
WordScorePoS
building0NN
sustainability0NN
world0.00276879546803864NN
move0NN
sustainable0.00138439773401932JJ
city0NN
partnership0NN
objective0NN
tool0NN
exist0VB
source0NN
impact0NN
structure0NN
emission0NN
datum0NN
collection0NN
greenhouse0NN
gas0NN
ecosystem0NN
service0.00795378154685591NN
rapid0JJ
plan0.0344256159926965NN
promotion0NN
support0NN
common0JJ
relevant0JJ
income0.166475342664294NN
density0NN
production0NN
geographic0JJ
green0JJ
regular0.064698038783335JJ
develop0VB
training0NN
alternative0NN
energy0NN
transport0NN
policy0.0848203862485078NN
land0NN
development0.0177346522508396NN
water0NN
wastewater0NN
waste0NN
local0JJ
national0.00886732612541978JJ
sense0.064698038783335NN
intervention0NN
cost0.0660824365173543NN
private0JJ
sector0NN
promote0.00886732612541978VB
include0VB
goal0NN
improvement0NN
infrastructure0NN
agreement0.02794560829783NN
financing0NN
climate0NN
political0.0792212041430275JJ
autonomy0NN
revenue0NN
expand0VB
implementation0.0475643836183697NN
report0NN
internet0NN
information0NN
approach0NN
create0VB
platform0NN

Label: 15, with a score of: 0.0669560141591583
WordScorePoS
building0NN
sustainability0NN
world0.00110699520004297NN
move0NN
sustainable0.00664197120025784JJ
city0NN
partnership0NN
objective0NN
tool0NN
exist0VB
source0.0232260916751625NN
impact0.0137637438743113NN
structure0NN
emission0NN
datum0NN
collection0NN
greenhouse0NN
gas0NN
ecosystem0.134075577362426NN
service0.0127200410376242NN
rapid0JJ
plan0.0137637438743113NN
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support0.00794883334420673NN
common0JJ
relevant0JJ
income0NN
density0NN
production0.016202004209163NN
geographic0JJ
green0JJ
regular0JJ
develop0VB
training0NN
alternative0NN
energy0.0116130458375813NN
transport0NN
policy0NN
land0.123873694868802NN
development0.0106357666046841NN
water0.016202004209163NN
wastewater0NN
waste0NN
local0.0275274877486226JJ
national0.00354525553489471JJ
sense0NN
intervention0NN
cost0NN
private0JJ
sector0NN
promote0.0106357666046841VB
include0VB
goal0NN
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agreement0.0223459295604043NN
financing0NN
climate0.032404008418326NN
political0JJ
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implementation0.0190167691930804NN
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This should be broad enough for cities in both developing and developed countries and structured to facilitate clear and publicly communicated public private partnerships

Label: 11, with a score of: 0.732517311254926
WordScorePoS
city0.607697021770652NN
develop0VB
country0NN
structure0NN
facilitate0VB
public0.0583751976558017NN
private0JJ
partnership0NN

Label: 17, with a score of: 0.649368104729999
WordScorePoS
city0NN
develop0VB
country0NN
structure0.048933054487825NN
facilitate0.0211360962287062VB
public0.0390556786919513NN
private0.0898760357768464JJ
partnership0.3914644359026NN

As urbanization continues apace and efforts toward sustainable development intensify cities will need greater cooperation and more partnerships

Label: 11, with a score of: 0.85391901287147
WordScorePoS
urbanization0.146277152173395NN
continue0.0537696560159885VB
sustainable0.00782503464617421JJ
development0.0100241617629471NN
city0.607697021770652NN
cooperation0NN
partnership0NN

Label: 17, with a score of: 0.484323524298812
WordScorePoS
urbanization0NN
continue0VB
sustainable0.0115176738164035JJ
development0.0469463607292177NN
city0NN
cooperation0.0183291958415182NN
partnership0.3914644359026NN

These partnerships are often likely to be ad hoc and transient but in other cases trust will grow ties will strengthen and cities and their agents should benefit significantly

Label: 11, with a score of: 0.839972315635022
WordScorePoS
partnership0NN
grow0.0229054731708393VB
strengthen0.0179828714372571VB
city0.607697021770652NN

Label: 17, with a score of: 0.522560820707149
WordScorePoS
partnership0.3914644359026NN
grow0VB
strengthen0.0120313639527759VB
city0NN

This report is intended to nurture stronger relationship between the varied players in the urban space

Label: 11, with a score of: 0.967791161483132
WordScorePoS
report0NN
urban0.27622591898666JJ
space0.04477822029736NN

The needs are enormous and urgent but so too are the opportunities

Label: 15, with a score of: 0.620269922079097
WordScorePoS
urgent0.0633470357346975JJ
opportunity0.0116130458375813NN

Label: 7, with a score of: 0.454754855595201
WordScorePoS
urgent0JJ
opportunity0.0549574626423028NN

REFERENCES References Alberti M

Label: 0, with a score of: 1

Marzluff E

Label: 0, with a score of: 1

Shulenberger G

Label: 0, with a score of: 1

Bradley C

Label: 0, with a score of: 1

Ryan and C

Label: 0, with a score of: 1

Zumbrunnen

Label: 0, with a score of: 1

Integrating humans into ecology Opportunities and challenges for studying urban ecosystems

Label: 11, with a score of: 0.806145855666552
WordScorePoS
integrate0.0537696560159885VB
human0.0273960270556257JJ
opportunity0.0164178645080222NN
challenge0.0947742174515357NN
study0NN
urban0.27622591898666JJ
ecosystem0NN

Label: 15, with a score of: 0.444141746016954
WordScorePoS
integrate0.0190167691930804VB
human0.0193783617722719JJ
opportunity0.0116130458375813NN
challenge0.0223459295604043NN
study0.0517339898971162NN
urban0JJ
ecosystem0.134075577362426NN

Bioscience

Label: 0, with a score of: 1

Bicknell Jane David Dodman and David Satterthwaite

Label: 0, with a score of: 1

Adapting Cities to Climate Change Understanding and Addressing the Development Challenge

Label: 11, with a score of: 0.689183185371854
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN
address0NN
development0.0100241617629471NN
challenge0.0947742174515357NN

Label: 13, with a score of: 0.645275688482151
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN
address0NN
development0NN
challenge0.0236803262479443NN

New York Earthscan

Label: 0, with a score of: 1

AINCA

Label: 0, with a score of: 1

Consejo Territorial de Planeación de Soacha

Label: 0, with a score of: 1

Política Pública Desarrollo Económico Incluyente del Municipio de Soacha

Label: 0, with a score of: 1

Departament of Cundinamarca Colombia

Label: 0, with a score of: 1

Bigio Anthony

Label: 0, with a score of: 1

Climate Change Adaptation and Natural Disasters Preparedness in the Coastal Cities of North Africa

Label: 11, with a score of: 0.690284114846983
WordScorePoS
climate0.0229054731708393NN
change0.0315914058171786NN
adaptation0.04477822029736NN
natural0.0229054731708393JJ
disaster0.157957029085893NN
coastal0JJ
city0.607697021770652NN
north0RB
africa0IN

Label: 13, with a score of: 0.623880352670564
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
adaptation0.0671298309154199NN
natural0.0171695137813101JJ
disaster0.0236803262479443NN
coastal0JJ
city0NN
north0RB
africa0IN

Label: 14, with a score of: 0.219759728519812
WordScorePoS
climate0.0166605993001162NN
change0NN
adaptation0NN
natural0JJ
disaster0NN
coastal0.265991560286306JJ
city0NN
north0RB
africa0IN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

AMWA Association of Metropolitan Water Agencies

Label: 6, with a score of: 0.909702007607857
WordScorePoS
water0.556409482163189NN
agency0NN

Label: 12, with a score of: 0.394265668850239
WordScorePoS
water0.186896403054502NN
agency0.0542519672965169NN

Implications of Climate Change for Urban Water Utilities

Label: 13, with a score of: 0.678433723368794
WordScorePoS
climate0.291881734282273NN
change0.402565546215053NN
urban0JJ
water0NN

Label: 6, with a score of: 0.543578997719365
WordScorePoS
climate0NN
change0NN
urban0JJ
water0.556409482163189NN

Label: 11, with a score of: 0.367851091514964
WordScorePoS
climate0.0229054731708393NN
change0.0315914058171786NN
urban0.27622591898666JJ
water0.0458109463416786NN

Label: 12, with a score of: 0.182586678887554
WordScorePoS
climate0NN
change0NN
urban0JJ
water0.186896403054502NN

Washington DC AMWA

Label: 0, with a score of: 1

Arup The Climate Group Accenture and Horizon and University of Nottingham

Label: 13, with a score of: 0.881370755494969
WordScorePoS
climate0.291881734282273NN
university0NN

Label: 7, with a score of: 0.347289978217427
WordScorePoS
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Ijjasz Vasquez and S

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Mehndiratta

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Co benefits of Climate change Mitigation Policies Literature Review and New Results

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Cheshire Paul and Stefano Magrini

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Urban Growth Drivers in Europe of Sticky People and Implicit Boundaries

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Journal of Economic Geography

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Chrysoulakis N

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Urban Metabolism and Resource Optimization in the Urban Fabric The BRIDGE Methodology

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In Proceedings of EnviroInfo Environmental Informatics and Industrial Ecology September Leuphana University of Lüneburg Germany Vol

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European Economic Review

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Hall

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Productivity and the Density of Economic Activity

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The American Economic Review

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Clean Air Task Force

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An Analysis of Diesel Air Pollution and Public Health in America

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Cromwell John Joel B

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Smith and Robert S

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Raucher

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Implications of Climate Change for Urban Water Utilities

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Washington DC Association of Metropolitan Water Agencies

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Quaas and S

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Baumgartner

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The relationship between resilience and sustainable development of ecological economic systems

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Ecological Economics

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Dhakal S

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Urban energy use and carbon emissions from cities in China and policy implications

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Dobbs Richard Sven Smit Jaana Remes James Manyika Charles Roxburgh and Alejandra Restrepo

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Urban World Mapping the Economic Power of Cities

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McKinsey Global Institute

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Dora C

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Health Burden of Urban Transport The Technical Challenge

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Sadhana

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Public and Private Financing of Infrastructure Policy Challenges in Mobilizing Finance

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ECARES European Centre for Advanced Research in Economics and Statistics

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Fitzhugh T

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and Richter B

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Quenching Urban Thirst Growing Cities and Their Impacts on Freshwater Ecosystems

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Bioscience REFERENCES Folke C

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Carpenter T

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Elmqvist

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Resilience and sustainable development building adaptive capacity in world of transformations

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Ambio

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Fuchs E

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Governing the Twenty First Century City

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Innovation With Impact Financing st Century Development

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Tanishima F

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Unander

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Polices for increasing energy efficiency Thirty years of experience in OECD countries

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Geothermal Energy Association

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Geothermal Energy International Market Update

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Urban Transport and Climate Change Action Plans An Overview

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Penguin

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sustainability0NN
universal0.0549574626423028JJ

Ecological Applications

Label: 0, with a score of: 1

Gusdorf F

Label: 0, with a score of: 1

Hallegatte

Label: 0, with a score of: 1

Compact or Spread Out Cities Urban Planning Taxation and the Vulnerability to Transportation Shocks

Label: 11, with a score of: 0.959308802388472
WordScorePoS
spread0NN
city0.607697021770652NN
urban0.27622591898666JJ
plan0.0583751976558017NN
taxation0NN
vulnerability0NN
transportation0.04477822029736NN
shock0NN

Energy Policy

Label: 7, with a score of: 0.908791081135247
WordScorePoS
energy0.494617163780726NN
policy0NN

Label: 12, with a score of: 0.283472944802822
WordScorePoS
energy0.133960988253756NN
policy0.0203215381458133NN

Gusdorf F

Label: 0, with a score of: 1

Hallegatte and A

Label: 0, with a score of: 1

Lahellec

Label: 0, with a score of: 1

Time and Space Matter How Urban Transitions Create Inequality

Label: 10, with a score of: 0.890850241676496
WordScorePoS
time0.0607862199751165NN
space0NN
urban0.0488696285210061JJ
create0VB
inequality0.635305170773079NN

Label: 11, with a score of: 0.421645990115827
WordScorePoS
time0NN
space0.04477822029736NN
urban0.27622591898666JJ
create0.0315914058171786VB
inequality0NN

Global Environment Change

Label: 13, with a score of: 0.881812598460147
WordScorePoS
global0.0375696197697663JJ
environment0NN
change0.402565546215053NN

Hallegatte Stéphane

Label: 0, with a score of: 1

Strategies to adapt to an uncertain climate change Global Environment Change

Label: 13, with a score of: 0.919896315081876
WordScorePoS
strategy0.0201523636533764NN
climate0.291881734282273NN
change0.402565546215053NN
global0.0375696197697663JJ
environment0NN

Hallegatte Stéphane Geoffrey Heal Marianne Fay and David Treguer

Label: 0, with a score of: 1

From Growth to Green Growth Framework

Label: 8, with a score of: 0.84305891746584
WordScorePoS
growth0.208565600657229NN
green0JJ
framework0.0428212738652355NN

Label: 10, with a score of: 0.409777888818424
WordScorePoS
growth0.11178243319132NN
green0JJ
framework0NN

Policy Research Working Paper

Label: 9, with a score of: 0.614312069943072
WordScorePoS
policy0.011275328195446NN
research0.104016094894625NN

Label: 10, with a score of: 0.451951980929092
WordScorePoS
policy0.0848203862485078NN
research0NN

Label: 17, with a score of: 0.341824749630281
WordScorePoS
policy0.0641521854453136NN
research0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Heal Geoffrey

Label: 0, with a score of: 1

Corporate Social Responsibility An Economic and Financial Framework

Label: 8, with a score of: 0.506438804574944
WordScorePoS
social0.0504071033101074JJ
responsibility0NN
economic0.128463821595706JJ
financial0.0370951943725875JJ
framework0.0428212738652355NN

Label: 1, with a score of: 0.507754884427155
WordScorePoS
social0.112994637345193JJ
responsibility0NN
economic0.0959899298575892JJ
financial0.0184786920895884JJ
framework0.0319966432858631NN

Label: 10, with a score of: 0.458731880610237
WordScorePoS
social0.101310366625194JJ
responsibility0NN
economic0.103276847978089JJ
financial0.0298222075472138JJ
framework0NN

Label: 5, with a score of: 0.404114958591976
WordScorePoS
social0.0285529457667019JJ
responsibility0.0911713013253052NN
economic0.0727679293221751JJ
financial0.0140083044329373JJ
framework0NN

The Geneva Papers on Risk and Insurance Issues and Practice

Label: 3, with a score of: 0.508698609392991
WordScorePoS
risk0.0703902516159815NN
insurance0NN
issue0.0249430985430622NN
practice0NN

Label: 8, with a score of: 0.429422379397646
WordScorePoS
risk0NN
insurance0.0804764811724088NN
issue0NN
practice0NN

Label: 12, with a score of: 0.375124036424293
WordScorePoS
risk0NN
insurance0NN
issue0NN
practice0.0703006268489394NN

Label: 11, with a score of: 0.337143385418809
WordScorePoS
risk0.0631828116343571NN
insurance0NN
issue0NN
practice0NN

Hollands Robert G

Label: 0, with a score of: 1

Will the Real Smart City Please Stand Up

Label: 11, with a score of: 0.994179944727542
WordScorePoS
city0.607697021770652NN
stand0VB

City

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

Holling C

Label: 0, with a score of: 1

Understanding the complexity of economic ecological and social systems

Label: 8, with a score of: 0.422191950348679
WordScorePoS
economic0.128463821595706JJ
social0.0504071033101074JJ
system0NN

Label: 1, with a score of: 0.615882652648454
WordScorePoS
economic0.0959899298575892JJ
social0.112994637345193JJ
system0.0519476926891779NN

Label: 10, with a score of: 0.482890526759705
WordScorePoS
economic0.103276847978089JJ
social0.101310366625194JJ
system0NN

Ecosystems

Label: 15, with a score of: 0.935065958316272
WordScorePoS
ecosystem0.134075577362426NN

Holm A

Label: 0, with a score of: 1

Geothermal Energy International Market Update

Label: 7, with a score of: 0.907650764247421
WordScorePoS
energy0.494617163780726NN
international0.00540783824576293JJ
market0NN

Label: 12, with a score of: 0.278359420409785
WordScorePoS
energy0.133960988253756NN
international0.0023966944644375JJ
market0.0169905820958638NN

Geothermal Energy Association

Label: 7, with a score of: 0.954096131466416
WordScorePoS
energy0.494617163780726NN

Label: 12, with a score of: 0.258405227354763
WordScorePoS
energy0.133960988253756NN

Hoornweg Daniel and Perinaz Bhada Tata

Label: 0, with a score of: 1

What Waste Waste Management around the World

Label: 12, with a score of: 0.809943984557836
WordScorePoS
waste0.138532104381853NN
management0.0339811641917276NN
world0.00464349782489754NN

Label: 11, with a score of: 0.384025068118561
WordScorePoS
waste0.0373517915079663NN
management0.0687164195125178NN
world0.00626002771693937NN

Label: 7, with a score of: 0.327507643589319
WordScorePoS
waste0.0625160381065121NN
management0NN
world0.00261936653839306NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Hoornweg Daniel and Perinaz Bhada Tata

Label: 0, with a score of: 1

In press

Label: 0, with a score of: 1

Hoornweg Daniel Mila Freire Marcus J

Label: 0, with a score of: 1

Lee Perinaz Bhada Tata and Belinda Yuen eds

Label: 0, with a score of: 1

Cities and Climate Change Responding to an Urgent Agenda

Label: 13, with a score of: 0.700386088788185
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN
urgent0.0335649154577099JJ
agenda0NN

Label: 11, with a score of: 0.637065421085888
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN
urgent0JJ
agenda0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Hoornweg Daniel Fernanda Ruiz Nuñez Mila Freire Natalie Palugyai Maria Villaveces and Eduardo Wills Herrera

Label: 0, with a score of: 1

City Indicators Now to Nanjing

Label: 11, with a score of: 0.997380969185671
WordScorePoS
city0.607697021770652NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Hoornweg Daniel Lorraine Sugar and Claudia Lorena Trejos Gomez

Label: 0, with a score of: 1

Cities and Greenhouse Gas Emissions Moving Forward

Label: 11, with a score of: 0.814115874985607
WordScorePoS
city0.607697021770652NN
greenhouse0NN
gas0NN
emission0.0373517915079663NN
move0NN

Label: 13, with a score of: 0.469069228089033
WordScorePoS
city0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
move0.0414107553022249NN

Environment and Urbanisation

Label: 12, with a score of: 0.715379678013361
WordScorePoS
environment0.079769385071606NN

Label: 8, with a score of: 0.530591258323478
WordScorePoS
environment0.0591643007226199NN

Hu S

Label: 0, with a score of: 1

Chang J

Label: 0, with a score of: 1

Li and Y

Label: 0, with a score of: 1

Qin

Label: 0, with a score of: 1

Energy for sustainable road transportation in China Challenges initiatives and policy implications

Label: 7, with a score of: 0.851306827505195
WordScorePoS
energy0.494617163780726NN
sustainable0.0104774661535722JJ
road0NN
transportation0NN
challenge0.0528748274225028NN
initiative0.0924643738905717NN
policy0NN

Label: 12, with a score of: 0.266673814691505
WordScorePoS
energy0.133960988253756NN
sustainable0.0162522423871414JJ
road0NN
transportation0.0332151194779746NN
challenge0NN
initiative0NN
policy0.0203215381458133NN

Label: 11, with a score of: 0.333891897354505
WordScorePoS
energy0.0492535935240667NN
sustainable0.00782503464617421JJ
road0.04477822029736NN
transportation0.04477822029736NN
challenge0.0947742174515357NN
initiative0NN
policy0.0136980135278128NN

Energy

Label: 7, with a score of: 0.954096131466416
WordScorePoS
energy0.494617163780726NN

Label: 12, with a score of: 0.258405227354763
WordScorePoS
energy0.133960988253756NN

Ibisch Pierre L

Label: 0, with a score of: 1

Alberto Vega E

Label: 0, with a score of: 1

and Thora Martina Herrmann eds

Label: 0, with a score of: 1

Interdependence of Biodiversity and Development under Global Ghange

Label: 15, with a score of: 0.791763823489442
WordScorePoS
biodiversity0.221714625071441NN
development0.0106357666046841NN
global0.00354525553489471JJ

Label: 2, with a score of: 0.333709861927701
WordScorePoS
biodiversity0.0714379839896614NN
development0.0159922815774215NN
global0.0119942111830662JJ

CBD Technical Series No

Label: 4, with a score of: 0.891435379283404
WordScorePoS
technical0.124876512346911JJ

Montreal Secretariat of the Convention on Biological Diversity ICLEI International Council for Local Environmental Initiatives

Label: 0, with a score of: 1

Financing the Resilient City demand driven approach to development disaster risk reduction and climate adaptation

Label: 11, with a score of: 0.830588176224212
WordScorePoS
financing0NN
resilient0.0747035830159326JJ
city0.607697021770652NN
demand0NN
drive0NN
approach0NN
development0.0100241617629471NN
disaster0.157957029085893NN
risk0.0631828116343571NN
reduction0.0268848280079943NN
climate0.0229054731708393NN
adaptation0.04477822029736NN

Label: 13, with a score of: 0.405686748069381
WordScorePoS
financing0NN
resilient0.0279982034978914JJ
city0NN
demand0NN
drive0.0414107553022249NN
approach0NN
development0NN
disaster0.0236803262479443NN
risk0NN
reduction0.0403047273067529NN
climate0.291881734282273NN
adaptation0.0671298309154199NN

Bonn an ICLEI Global Report

Label: 12, with a score of: 0.639079031464264
WordScorePoS
global0.022306853314744JJ
report0.0409791940864438NN

Label: 3, with a score of: 0.451726883613488
WordScorePoS
global0.013959557403375JJ
report0.0307735781890304NN

IEA International Energy Agency

Label: 7, with a score of: 0.922127245443873
WordScorePoS
international0.00540783824576293JJ
energy0.494617163780726NN
agency0NN

Label: 12, with a score of: 0.351515125865195
WordScorePoS
international0.0023966944644375JJ
energy0.133960988253756NN
agency0.0542519672965169NN

World Energy Outlook

Label: 7, with a score of: 0.949045013732928
WordScorePoS
world0.00261936653839306NN
energy0.494617163780726NN

Label: 12, with a score of: 0.264545922057536
WordScorePoS
world0.00464349782489754NN
energy0.133960988253756NN

Paris OECD

Label: 12, with a score of: 0.892539381531446
WordScorePoS
paris0NN
oecd0.108503934593034NN

Label: 13, with a score of: 0.450969458406513
WordScorePoS
paris0.0548233071065585NN
oecd0NN

IEA and OECD

Label: 12, with a score of: 1
WordScorePoS
oecd0.108503934593034NN

World Energy Outlook

Label: 7, with a score of: 0.949045013732928
WordScorePoS
world0.00261936653839306NN
energy0.494617163780726NN

Label: 12, with a score of: 0.264545922057536
WordScorePoS
world0.00464349782489754NN
energy0.133960988253756NN

Paris OECD

Label: 12, with a score of: 0.892539381531446
WordScorePoS
paris0NN
oecd0.108503934593034NN

Label: 13, with a score of: 0.450969458406513
WordScorePoS
paris0.0548233071065585NN
oecd0NN

Jaffe A

Label: 0, with a score of: 1

Stavins

Label: 0, with a score of: 1

The Energy Paradox and the Diffusion of Conservation Technology

Label: 7, with a score of: 0.900503469782853
WordScorePoS
energy0.494617163780726NN
conservation0NN
technology0.109914925284606NN

Label: 12, with a score of: 0.19954661947649
WordScorePoS
energy0.133960988253756NN
conservation0NN
technology0NN

Resource and Energy Economics

Label: 7, with a score of: 0.881323063495366
WordScorePoS
resource0NN
energy0.494617163780726NN
economics0NNS

Label: 12, with a score of: 0.312959625424413
WordScorePoS
resource0.0416785799329393NN
energy0.133960988253756NN
economics0NNS

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS JICA Japan International Cooperation Agency

Label: 11, with a score of: 0.76053492064872
WordScorePoS
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
international0JJ
cooperation0NN
agency0NN

Label: 17, with a score of: 0.323451978639715
WordScorePoS
urban0JJ
development0.0469463607292177NN
knowledge0.0499801157438617NN
international0.00648516099948979JJ
cooperation0.0183291958415182NN
agency0NN

The Study on Monitoring and Early Warning Systems for Landslides and Floods

Label: 2, with a score of: 0.576950675910902
WordScorePoS
study0NN
monitor0NN
warn0VB
system0.0756001353333673NN
flood0.0357189919948307NN

Label: 15, with a score of: 0.43228885399789
WordScorePoS
study0.0517339898971162NN
monitor0NN
warn0VB
system0NN
flood0.0316735178673487NN

Label: 17, with a score of: 0.420088136280723
WordScorePoS
study0NN
monitor0.0599173571845643NN
warn0VB
system0.0211360962287062NN
flood0NN

Cooperation Project

Label: 12, with a score of: 0.681163278842555
WordScorePoS
cooperation0.0203215381458133NN
project0.0664302389559493NN

Label: 6, with a score of: 0.529464708924449
WordScorePoS
cooperation0.0255958462813622NN
project0.041835863322702NN

Label: 13, with a score of: 0.344168382149095
WordScorePoS
cooperation0.0102677744436108NN
project0.0335649154577099NN

Municipality of Soacha Colombia

Label: 0, with a score of: 1

Kenworthy J

Label: 0, with a score of: 1

The Eco City Ten Key Transport and Planning Dimensions for Sustainable City Development

Label: 11, with a score of: 0.971132556039881
WordScorePoS
city0.607697021770652NN
key0NN
transport0.0747035830159326NN
plan0.0583751976558017NN
dimension0NN
sustainable0.00782503464617421JJ
development0.0100241617629471NN

Label: 1, with a score of: 0.144509936597208
WordScorePoS
city0NN
key0NN
transport0NN
plan0NN
dimension0.181686110904276NN
sustainable0.00514687440545096JJ
development0.0164833460638534NN

Environment and Urbanization

Label: 11, with a score of: 0.848019086847873
WordScorePoS
environment0.0268848280079943NN
urbanization0.146277152173395NN

Label: 12, with a score of: 0.390651348615785
WordScorePoS
environment0.079769385071606NN
urbanization0NN

Johnson Controls IFMA International Facility Management Association and ULI Urban Land Institute

Label: 11, with a score of: 0.802009661275315
WordScorePoS
control0NN
international0JJ
facility0NN
management0.0687164195125178NN
urban0.27622591898666JJ
land0.0389167984372011NN

Label: 15, with a score of: 0.420343885450684
WordScorePoS
control0.0264204925485796NN
international0.00228545753063677JJ
facility0NN
management0.0486060126274891NN
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land0.123873694868802NN

Fifth Annual Global Energy Efficiency Survey

Label: 7, with a score of: 0.929714065389089
WordScorePoS
global0.0251662980592747JJ
energy0.494617163780726NN
survey0NN

Label: 12, with a score of: 0.279509489893985
WordScorePoS
global0.022306853314744JJ
energy0.133960988253756NN
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Kessides C

Label: 0, with a score of: 1

The Urban Transition in Sub Sub Saharan Africa Implications for Economic Growth and Poverty Reduction

Label: 1, with a score of: 0.559405134471356
WordScorePoS
urban0JJ
saharan0.0442083771593746JJ
africa0IN
economic0.0959899298575892JJ
growth0.0519476926891779NN
poverty0.269969049497485NN
reduction0NN

Label: 8, with a score of: 0.495458250987947
WordScorePoS
urban0JJ
saharan0JJ
africa0IN
economic0.128463821595706JJ
growth0.208565600657229NN
poverty0.0722601961736204NN
reduction0NN

Label: 11, with a score of: 0.43390970082799
WordScorePoS
urban0.27622591898666JJ
saharan0JJ
africa0IN
economic0.0389167984372011JJ
growth0NN
poverty0.0164178645080222NN
reduction0.0268848280079943NN

Label: 10, with a score of: 0.447395688737198
WordScorePoS
urban0.0488696285210061JJ
saharan0JJ
africa0IN
economic0.103276847978089JJ
growth0.11178243319132NN
poverty0.05809266143877NN
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Washington DC Cities Alliance

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

Kalsi N

Label: 0, with a score of: 1

Kiran and S

Label: 0, with a score of: 1

Vaidya

Label: 0, with a score of: 1

Effective Governance for Good Governance in India

Label: 16, with a score of: 1
WordScorePoS
governance0.107490825192354NN

Int

Label: 0, with a score of: 1

Rev

Label: 0, with a score of: 1

of Bus

Label: 0, with a score of: 1

Res

Label: 0, with a score of: 1

Pap

Label: 0, with a score of: 1

Kamal Chaoui Lamia and Alexis Robert eds

Label: 0, with a score of: 1

Competitive Cities and Climate Change

Label: 13, with a score of: 0.693914856106419
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN

Label: 11, with a score of: 0.661686204646705
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN

OECD Regional Development Working Paper No

Label: 12, with a score of: 0.658188308218
WordScorePoS
oecd0.108503934593034NN
regional0JJ
development0.022306853314744NN

Label: 17, with a score of: 0.448912582232632
WordScorePoS
oecd0NN
regional0.0422721924574123JJ
development0.0469463607292177NN

Label: 1, with a score of: 0.344318005776495
WordScorePoS
oecd0NN
regional0.0519476926891779JJ
development0.0164833460638534NN

Paris OECD

Label: 12, with a score of: 0.892539381531446
WordScorePoS
paris0NN
oecd0.108503934593034NN

Label: 13, with a score of: 0.450969458406513
WordScorePoS
paris0.0548233071065585NN
oecd0NN

Karlenzig Warren

Label: 0, with a score of: 1

Cities The Death of Sprawl

Label: 11, with a score of: 0.948802830876689
WordScorePoS
city0.607697021770652NN
death0.0315914058171786NN

Label: 3, with a score of: 0.28729253428105
WordScorePoS
city0NN
death0.193573191943949NN

The Post Carbon Reader Series Cities Towns and Suburbs

Label: 11, with a score of: 0.979052320293018
WordScorePoS
post0NN
carbon0.0373517915079663NN
city0.607697021770652NN

Santa Rosa California

Label: 0, with a score of: 1

Keith Adam and Steve Bochinger

Label: 0, with a score of: 1

Satellite based Earth Observation Market Prospects to

Label: 14, with a score of: 0.74151028806719
WordScorePoS
base0.058665107245718NN
earth0.0977100948994686NN
market0.0333211986002325NN
prospects0NNS

Label: 9, with a score of: 0.382729901927139
WordScorePoS
base0NN
earth0NN
market0.0377086395700813NN
prospects0.0602029956716497NNS

Label: 2, with a score of: 0.357108122178776
WordScorePoS
base0NN
earth0NN
market0.0913569596328122NN
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Euroconsult

Label: 0, with a score of: 1

Kennedy C

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Pincetl and P

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OCHA United Nations Office for the Coordination of Humanitarian Affairs

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Towards Green Growth

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Assessing the Costs of Adaptation to Climate Change review of the UNFCCC and Other Recent Estimates

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Internet matters the Net weeping impact on growth jobs and prosperity

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REN Renewable Energy Policy Network for the st Century

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Renewables Global Status Report

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Republic of the Philippines

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The Local Government Code of

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Manila Government of the Philippines

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Rifkin Jermy

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The Third Industrial Revolution How Lateral Power Is Transforming Energy the Economy and the World

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Palgrave MacMillan

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Delfs

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Going Green How Cities are Leading the Next Economy

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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Rodrigue Jean Paul

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The Geography of Transport Systems rd Edition

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Nicholas Stern and D

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Policy Brief

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Sassen S

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savage sorting of winners and losers contemporary versions of primitive accumulation

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Globalizations

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Satterwhaite David Saleemul Huq Hannah Reid Mark Pelling and Patricia Romero Lankao

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Adapting to Climate Change in Urban Areas the Possibilities and Constraints in Low and Middle Income Nations

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Sustainable Urban Metabolism for Europe SUME

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Paper presented at the World Bank Fifth Urban Research Symposium Marseille France June

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Sedgley Norman and Bruce Elmslie

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Informational Cities Analysis and Construction of Cities in the Knowledge Society

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Fleming

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Journal of Urban Economics

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International Journal of Climate Change Strategies and Management

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TEEB Manual for Cities Ecosystem Services in Urban Management

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Bonn UNEP

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ULI Urban Land Institute

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UN Habitat

Label: 14, with a score of: 0.716904600087647
WordScorePoS
habitat0.0401833161726777NN

Label: 15, with a score of: 0.697171280513742
WordScorePoS
habitat0.0390772412228479NN

State of the World Cities Bridging the Urban Divide

Label: 11, with a score of: 0.996223973427951
WordScorePoS
world0.00626002771693937NN
city0.607697021770652NN
urban0.27622591898666JJ

London Earthscan

Label: 0, with a score of: 1

USGBC U

Label: 0, with a score of: 1

Green Building Council and Green Building Council Brasil

Label: 17, with a score of: 0.655895938124512
WordScorePoS
green0JJ
building0.149940347231585NN

Label: 11, with a score of: 0.522658716749138
WordScorePoS
green0.04477822029736JJ
building0.0747035830159326NN

Label: 16, with a score of: 0.480267012194921
WordScorePoS
green0JJ
building0.109790895760528NN

Building the Green Economy from the Ground Up Sustainable Cities and the Built Environment

Label: 11, with a score of: 0.897496903307657
WordScorePoS
building0.0747035830159326NN
green0.04477822029736JJ
economy0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN
build0VB
environment0.0268848280079943NN

Label: 17, with a score of: 0.249071913083785
WordScorePoS
building0.149940347231585NN
green0JJ
economy0NN
sustainable0.0115176738164035JJ
city0NN
build0.0499801157438617VB
environment0NN

USGBC and Green Building Council Brasil

Label: 17, with a score of: 0.655895938124513
WordScorePoS
green0JJ
building0.149940347231585NN

Label: 11, with a score of: 0.522658716749138
WordScorePoS
green0.04477822029736JJ
building0.0747035830159326NN

Label: 16, with a score of: 0.480267012194921
WordScorePoS
green0JJ
building0.109790895760528NN

Vandeweghe J

Label: 0, with a score of: 1

Spatial Analysis of Residential Greenhouse Gas Emissions in the Toronto Census Metropolitan Area

Label: 13, with a score of: 0.764541357426776
WordScorePoS
residential0JJ
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN

Label: 7, with a score of: 0.491735761280995
WordScorePoS
residential0JJ
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN

Label: 12, with a score of: 0.407670355368944
WordScorePoS
residential0.0542519672965169JJ
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
emission0.0554128417527413NN

Journal of Industrial Ecology

Label: 9, with a score of: 0.991644007426698
WordScorePoS
industrial0.318320101004075JJ

Vergara Sintana E

Label: 0, with a score of: 1

and George Tchobanoglous

Label: 0, with a score of: 1

Municipal Solid Waste and the Environment

Label: 12, with a score of: 0.781899618715175
WordScorePoS
municipal0JJ
waste0.138532104381853NN
environment0.079769385071606NN

Label: 11, with a score of: 0.492042511169129
WordScorePoS
municipal0.0731385760866977JJ
waste0.0373517915079663NN
environment0.0268848280079943NN

Annual Review of Environment and Resources

Label: 12, with a score of: 0.514770694009793
WordScorePoS
review0NN
environment0.079769385071606NN
resource0.0416785799329393NN

Label: 5, with a score of: 0.410648520688801
WordScorePoS
review0.0688661930304451NN
environment0NN
resource0.0280166088658745NN

Label: 17, with a score of: 0.316005115191093
WordScorePoS
review0.036961556179878NN
environment0NN
resource0.0375923735942499NN

Label: 1, with a score of: 0.313295958511769
WordScorePoS
review0NN
environment0NN
resource0.0739147683583536NN

Label: 11, with a score of: 0.304481748878181
WordScorePoS
review0NN
environment0.0268848280079943NN
resource0.0449504361447405NN

Label: 8, with a score of: 0.30318516350667
WordScorePoS
review0NN
environment0.0591643007226199NN
resource0.0123650647908625NN

Warren Rhodes K

Label: 0, with a score of: 1

Koenig

Label: 0, with a score of: 1

Escalating Trends in the Urban Metabolism of Hong Kong

Label: 11, with a score of: 0.984707888374301
WordScorePoS
urban0.27622591898666JJ

Ambio

Label: 0, with a score of: 1

WFEO ComTech World Federation of Engineering Organizations Committee on Technology

Label: 17, with a score of: 0.618589336827265
WordScorePoS
world0.00418824502414673NN
engineering0NN
organization0.0422721924574123NN
technology0.109843026967392NN

Label: 7, with a score of: 0.445367690310218
WordScorePoS
world0.00261936653839306NN
engineering0NN
organization0NN
technology0.109914925284606NN

Label: 4, with a score of: 0.401885655116439
WordScorePoS
world0.0017440716962658NN
engineering0.0815069365350084NN
organization0NN
technology0.0182963616752599NN

Label: 9, with a score of: 0.331099193123047
WordScorePoS
world0.00257642711761688NN
engineering0NN
organization0NN
technology0.0810848128688654NN

Engineers and Sustainable Development

Label: 17, with a score of: 0.518730083611407
WordScorePoS
sustainable0.0115176738164035JJ
development0.0469463607292177NN

Label: 16, with a score of: 0.408418395227058
WordScorePoS
sustainable0.00920028227269801JJ
development0.0368309542451156NN

Label: 9, with a score of: 0.372850238346778
WordScorePoS
sustainable0.00901749491165909JJ
development0.0330049940546426NN

Label: 12, with a score of: 0.342120811416312
WordScorePoS
sustainable0.0162522423871414JJ
development0.022306853314744NN

Label: 4, with a score of: 0.325165429869451
WordScorePoS
sustainable0.00872035848132902JJ
development0.0279277626235394NN

Washington DC WFEO ComTech

Label: 0, with a score of: 1

Wilbanks T

Label: 0, with a score of: 1

Sathaye

Label: 0, with a score of: 1

Integrating mitigation and adaptation as responses to climate change synthesis

Label: 13, with a score of: 0.932488756241496
WordScorePoS
integrate0.0201523636533764VB
mitigation0.0828215106044499NN
adaptation0.0671298309154199NN
response0.0548233071065585NN
climate0.291881734282273NN
change0.402565546215053NN

Mitig

Label: 0, with a score of: 1

Adapt

Label: 0, with a score of: 1

Strat

Label: 0, with a score of: 1

Glob

Label: 0, with a score of: 1

Change

Label: 13, with a score of: 0.943750661932677
WordScorePoS
change0.402565546215053NN

Williams

Label: 0, with a score of: 1

Kahhat B

Label: 0, with a score of: 1

Allenby E

Label: 0, with a score of: 1

Kavazanjian J

Label: 0, with a score of: 1

Kim and M

Label: 0, with a score of: 1

Xu

Label: 0, with a score of: 1

Environmental social and economic implications of global reuse and recycling of personal computers

Label: 8, with a score of: 0.381804264603875
WordScorePoS
environmental0.0295821503613099JJ
social0.0504071033101074JJ
economic0.128463821595706JJ
global0.0220597476338268JJ
reuse0NN
recycle0VB
personal0JJ

Label: 12, with a score of: 0.529386416807215
WordScorePoS
environmental0.0598270388037045JJ
social0.0169905820958638JJ
economic0.0433009429988926JJ
global0.022306853314744JJ
reuse0.0409791940864438NN
recycle0.0819583881728876VB
personal0.0542519672965169JJ

Label: 1, with a score of: 0.419369927922064
WordScorePoS
environmental0.0442083771593746JJ
social0.112994637345193JJ
economic0.0959899298575892JJ
global0JJ
reuse0NN
recycle0VB
personal0JJ

Label: 10, with a score of: 0.400284836685471
WordScorePoS
environmental0.0237821918091848JJ
social0.101310366625194JJ
economic0.103276847978089JJ
global0.0133009891881297JJ
reuse0NN
recycle0VB
personal0JJ

Label: 6, with a score of: 0.357477555114265
WordScorePoS
environmental0JJ
social0JJ
economic0JJ
global0.00936547854413134JJ
reuse0.103230094596636NN
recycle0.103230094596636VB
personal0JJ

Environmental Science Technology Wolman A

Label: 17, with a score of: 0.611937107155041
WordScorePoS
environmental0JJ
science0.0599173571845643NN
technology0.109843026967392NN

Label: 7, with a score of: 0.39621152925536
WordScorePoS
environmental0JJ
science0NN
technology0.109914925284606NN

Label: 9, with a score of: 0.372059092554426
WordScorePoS
environmental0.0221298700466866JJ
science0NN
technology0.0810848128688654NN

Label: 11, with a score of: 0.355236603926879
WordScorePoS
environmental0.0537696560159885JJ
science0.04477822029736NN
technology0NN

The Metabolism of Cities

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

Scientific American

Label: 0, with a score of: 1

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Cities on the move World Bank urban transport strategy review

Label: 11, with a score of: 0.970016700704608
WordScorePoS
city0.607697021770652NN
move0NN
world0.00626002771693937NN
bank0NN
urban0.27622591898666JJ
transport0.0747035830159326NN
strategy0NN
review0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Public and Private Sector Roles in the Supply of Electricity Services

Label: 5, with a score of: 0.498635878323933
WordScorePoS
public0.0727679293221751NN
private0.0558185411034276JJ
sector0.0571058915334038NN
supply0NN
electricity0NN
service0.022416649027415NN

Label: 17, with a score of: 0.419080741587473
WordScorePoS
public0.0390556786919513NN
private0.0898760357768464JJ
sector0.0459744293658165NN
supply0NN
electricity0NN
service0NN

Label: 7, with a score of: 0.384654236725736
WordScorePoS
public0NN
private0JJ
sector0NN
supply0.0528748274225028NN
electricity0.0625160381065121NN
service0.0451471467893167NN

Label: 9, with a score of: 0.371352365653869
WordScorePoS
public0.0160169090870187NN
private0.0368585655748969JJ
sector0.0565629593551219NN
supply0NN
electricity0.030745604615229NN
service0.0148023490369527NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Reforming Infrastructure Privatization regulation and competition

Label: 9, with a score of: 0.735215882694947
WordScorePoS
reform0NN
infrastructure0.192202909044225NN
regulation0NN

Label: 5, with a score of: 0.441533212644039
WordScorePoS
reform0.0911713013253052NN
infrastructure0.024255976440725NN
regulation0NN

Label: 10, with a score of: 0.373872874752155
WordScorePoS
reform0NN
infrastructure0NN
regulation0.0977392570420122NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Project Appraisal Document for the Lagos Metropolitan Development and Governance Project

Label: 12, with a score of: 0.576664594348514
WordScorePoS
project0.0664302389559493NN
document0NN
development0.022306853314744NN
governance0NN

Label: 16, with a score of: 0.536358135034252
WordScorePoS
project0NN
document0NN
development0.0368309542451156NN
governance0.107490825192354NN

Label: 5, with a score of: 0.385268434695028
WordScorePoS
project0NN
document0.0911713013253052NN
development0.0124956749436837NN
governance0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Decentralization in Client Countries An evaluation of World Bank Support

Label: 2, with a score of: 0.710018937113748
WordScorePoS
country0NN
world0.0124838525528648NN
bank0.0881366997038154NN
support0.00896409157262106NN

Label: 17, with a score of: 0.558898514834739
WordScorePoS
country0NN
world0.00418824502414673NN
bank0.036961556179878NN
support0.0451108483130998NN

Washington DC World Bank Independent Evaluation Group

Label: 2, with a score of: 0.918499615257702
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Development Report Reshaping Economic Geography

Label: 8, with a score of: 0.470441895604173
WordScorePoS
world0.00172202218602071NN
development0.0055149369084567NN
report0NN
economic0.128463821595706JJ

Label: 10, with a score of: 0.429116447598586
WordScorePoS
world0.00276879546803864NN
development0.0177346522508396NN
report0NN
economic0.103276847978089JJ

Label: 1, with a score of: 0.398839222072936
WordScorePoS
world0.00257343720272548NN
development0.0164833460638534NN
report0NN
economic0.0959899298575892JJ

Label: 12, with a score of: 0.385609290259016
WordScorePoS
world0.00464349782489754NN
development0.022306853314744NN
report0.0409791940864438NN
economic0.0433009429988926JJ

Label: 5, with a score of: 0.315877931760631
WordScorePoS
world0.00585260443759492NN
development0.0124956749436837NN
report0NN
economic0.0727679293221751JJ

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

A City wide Approach to Carbon Finance

Label: 11, with a score of: 0.97361228594147
WordScorePoS
city0.607697021770652NN
wide0JJ
approach0NN
carbon0.0373517915079663NN
finance0NN

Carbon Partnership Facility Innovation Series

Label: 17, with a score of: 0.907238563628351
WordScorePoS
carbon0NN
partnership0.3914644359026NN
facility0NN
innovation0.0499801157438617NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS World Bank

Label: 11, with a score of: 0.75229357448256
WordScorePoS
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
world0.00626002771693937NN
bank0NN

Label: 2, with a score of: 0.453168278537049
WordScorePoS
urban0JJ
development0.0159922815774215NN
knowledge0.0595900566394012NN
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.355112160155774
WordScorePoS
urban0JJ
development0.0469463607292177NN
knowledge0.0499801157438617NN
world0.00418824502414673NN
bank0.036961556179878NN

The Costs to Developing Countries of Adapting to Climate Change New Methods and Estimates

Label: 13, with a score of: 0.922878527380982
WordScorePoS
cost0.0279982034978914NN
develop0VB
country0NN
climate0.291881734282273NN
change0.402565546215053NN
estimate0NN

The Global Report of the Economics of Adaptation to Climate Change Study

Label: 13, with a score of: 0.911362517647505
WordScorePoS
global0.0375696197697663JJ
report0NN
economics0NNS
adaptation0.0671298309154199NN
climate0.291881734282273NN
change0.402565546215053NN
study0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Development Report Development and Climate Change

Label: 13, with a score of: 0.886184521808695
WordScorePoS
world0.00351929966767313NN
development0NN
report0NN
climate0.291881734282273NN
change0.402565546215053NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Natural Hazards Unnatural Disasters The Economics of Effective Prevention

Label: 11, with a score of: 0.59428106373088
WordScorePoS
natural0.0229054731708393JJ
hazard0NN
disaster0.157957029085893NN
economics0NNS
prevention0NN

Label: 6, with a score of: 0.488811705173311
WordScorePoS
natural0.0214003646985842JJ
hazard0NN
disaster0.0590310971343993NN
economics0.0683326727248364NNS
prevention0NN

Label: 13, with a score of: 0.314364442274742
WordScorePoS
natural0.0171695137813101JJ
hazard0.0548233071065585NN
disaster0.0236803262479443NN
economics0NNS
prevention0NN

Label: 3, with a score of: 0.303349028451834
WordScorePoS
natural0JJ
hazard0NN
disaster0NN
economics0NNS
prevention0.0923207345670913NN

Label: 12, with a score of: 0.302133952448412
WordScorePoS
natural0.0509717462875914JJ
hazard0NN
disaster0NN
economics0NNS
prevention0.0409791940864438NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

Cities and Climate Change An Urgent Agenda

Label: 13, with a score of: 0.700386088788185
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN
urgent0.0335649154577099JJ
agenda0NN

Label: 11, with a score of: 0.637065421085888
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN
urgent0JJ
agenda0NN

Washington DC World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
world0.00418824502414673NN
bank0.036961556179878NN

World Bank

Label: 2, with a score of: 0.918499615257701
WordScorePoS
world0.0124838525528648NN
bank0.0881366997038154NN

Label: 17, with a score of: 0.375629786620639
WordScorePoS
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Low Carbon Cities in China

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carbon0.0373517915079663NN
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Washington DC World Bank

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world0.0124838525528648NN
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World Bank

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Inclusive Green Growth The Pathway to Sustainable Development

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inclusive0.0857932776726581JJ
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sustainable0.00920028227269801JJ
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inclusive0.0583751976558017JJ
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Washington DC World Bank

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world0.0124838525528648NN
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Label: 17, with a score of: 0.375629786620639
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world0.00418824502414673NN
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World Bank

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world0.0124838525528648NN
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Urban Risk Assessments An Approach for Understanding Disaster and Climate Risk in Cities

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urban0.27622591898666JJ
risk0.0631828116343571NN
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climate0.0229054731708393NN
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WordScorePoS
urban0JJ
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Washington DC World Bank

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world0.0124838525528648NN
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World Bank

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world0.0124838525528648NN
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Label: 17, with a score of: 0.375629786620639
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world0.00418824502414673NN
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The Little Green Data Book

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Label: 9, with a score of: 0.419202995527437
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green0.0335649154577099JJ
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Label: 12, with a score of: 0.306192234691874
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Washington DC World Bank

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Label: 17, with a score of: 0.375629786620639
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World Bank Insitute ed

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Label: 17, with a score of: 0.375629786620639
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Open Government special issue

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Development Outreach September

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development0.0469463607292177NN

Label: 16, with a score of: 0.432469942010676
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Label: 9, with a score of: 0.38754542632484
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World Commission on Environment and Development

Label: 12, with a score of: 0.56118842093642
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Label: 8, with a score of: 0.349172697271139
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Label: 14, with a score of: 0.322858878061617
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Label: 4, with a score of: 0.313580408438738
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Label: 9, with a score of: 0.303476278513786
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Our Common Future

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Label: 3, with a score of: 0.472460640650017
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Label: 14, with a score of: 0.417109831357647
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Oxford Oxford University Press

Label: 4, with a score of: 1
WordScorePoS
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WRI World Resources Institute

Label: 1, with a score of: 0.519939653873893
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world0.00257343720272548NN
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Label: 14, with a score of: 0.419891067512056
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Label: 11, with a score of: 0.348110544098215
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Label: 12, with a score of: 0.314881031649992
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World Greenhouse Gas Emissions in

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WRI

Label: 0, with a score of: 1

Zenghelis D

Label: 0, with a score of: 1

The Economics of Network Powered Growth

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Label: 10, with a score of: 0.421077074838689
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Cisco

Label: 0, with a score of: 1

Zenghelis D

Label: 0, with a score of: 1

Networked Solutions for st Century Challenges The Economics of Complexity and Scarcity and the Role of Networked Innovation

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solution0NN
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Cisco

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Zenghelis C

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A macroeconmic plan for green recovery

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Label: 11, with a score of: 0.589540074871432
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Label: 13, with a score of: 0.358548615148389
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London Grantham Research Institute on Climate Change and the Environment Centre for Climate Change Economics and Policy

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Zenghelis D

Label: 0, with a score of: 1

A Strategy for Restoring Confidence and Economic Growth through Green Investment and Innovation

Label: 8, with a score of: 0.707624837197719
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strategy0.0295821503613099NN
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Label: 10, with a score of: 0.351296303434673
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Label: 1, with a score of: 0.343070064305906
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Label: 9, with a score of: 0.326526481901134
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London Grantham Research Institute on Climate Change and the Environment Centre for Climate Change Economics and Policy

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WordScorePoS
research0NN
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Label: 7, with a score of: 0.317056952277259
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research0.0528748274225028NN
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ANNEX ANNEXES URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Total and Urban Global Populations by Region Total and Urban Population AFR EAP OECD SAR LCR Urban MNA Urban ECA LCR MNA AFR Urban EAP Urban ECA Urban OECD Urban SAR Urban Source Reprinted from Hoornweg and Bhada Tata in press

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urban0.27622591898666JJ
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ANNEX ANNEX The Green City Index Series Urbanization has enormous environmental consequences both global and local

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green0.04477822029736JJ
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Already city dwellers are estimated to be responsible for up to percent of the world greenhouse gas emissions IEA

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city0.607697021770652NN
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Sprawling urban development consumes arable land and vital green spaces

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urban0.27622591898666JJ
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WordScorePoS
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Burgeoning numbers of city residents put pressure on water infrastructure waste management sewer systems and transport networks

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In order to tackle climate change avoid lasting damage to vital ecology and maintain the health and well being of billions of people solutions to these problems must be sought at the city level

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The development of such solutions however will depend on the knowledge generated from benchmarking urban performance and sharing of best practices across cities

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Many such benchmarking initiatives and tools are emerging from communitybased organizations e

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Label: 17, with a score of: 0.51476994115596
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ICLEI academia and the private sector addressing myriad aspects of urban performance and sustainability

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The Green City Index is one of these benchmarking tools

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WordScorePoS
green0.04477822029736JJ
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The challenge moving forward is twofold these tools and systems need to be harmonized and methods for data collection and analysis standardized and city participation needs to become more universal easier and based on less ad hoc criteria for selection

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Conducted as research project by the Economist Intelligence Unit and sponsored by Siemens the Green City Index series has sought to focus attention on urban environmental sustainability

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The series began with reports on cities Europe in and has since analyzed total of more than cities in the United States and Canada Asia Latin America and Africa with Australia and New Zealand

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The many lessons contained in these reports are intended to help cities understand their strengths and weaknesses and learn from each other as they debate policies and strategies to minimize their environmental footprint while at the same time accommodating population growth and promoting economic opportunity

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Label: 4, with a score of: 0.212286243734614
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Label: 10, with a score of: 0.422116586505557
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Methodology What the Green City Index Measures The Green City Index methodology was developed by the Economist Intelligence Unit in cooperation with Siemens

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green0.04477822029736JJ
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Independent urban sustainability experts also advised on the methodology and provided insight into the key findings for each region

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urban0.27622591898666JJ
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Label: 1, with a score of: 0.484846592708588
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Label: 3, with a score of: 0.328488830679276
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Cities were selected for their size and importance mainly capital cities and large population or business centers

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city0.607697021770652NN
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They were chosen independently rather than relying on requests from city governments to be included or excluded in order to enhance each index credibility and comparability

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enhance0.0112376090361851VB

The Green City Index series measures cities on approximately indicators across eight to nine categories covering carbon dioxide emissions energy consumption buildings and land use transport water sanitation waste management air quality and environmental governance

Label: 11, with a score of: 0.724123506938984
WordScorePoS
green0.04477822029736JJ
city0.607697021770652NN
measure0NN
approximately0RB
cover0NN
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
energy0.0492535935240667NN
consumption0.08955644059472NN
building0.0747035830159326NN
land0.0389167984372011NN
transport0.0747035830159326NN
water0.0458109463416786NN
sanitation0NN
waste0.0373517915079663NN
management0.0687164195125178NN
air0.0373517915079663NN
quality0.0136980135278128NN
environmental0.0537696560159885JJ
governance0NN

Label: 12, with a score of: 0.412066350660978
WordScorePoS
green0.0332151194779746JJ
city0NN
measure0NN
approximately0RB
cover0NN
carbon0NN
dioxide0NN
emission0.0554128417527413NN
energy0.133960988253756NN
consumption0.332151194779746NN
building0NN
land0.0144336476662975NN
transport0.0277064208763707NN
water0.186896403054502NN
sanitation0NN
waste0.138532104381853NN
management0.0339811641917276NN
air0.0554128417527413NN
quality0.0203215381458133NN
environmental0.0598270388037045JJ
governance0NN

Label: 6, with a score of: 0.381073434073964
WordScorePoS
green0JJ
city0NN
measure0NN
approximately0RB
cover0NN
carbon0NN
dioxide0NN
emission0NN
energy0.0153390539205676NN
consumption0NN
building0.0348974218717993NN
land0NN
transport0NN
water0.556409482163189NN
sanitation0.334686906581616NN
waste0NN
management0.0428007293971684NN
air0NN
quality0.0255958462813622NN
environmental0JJ
governance0NN

Label: 7, with a score of: 0.269741319256196
WordScorePoS
green0JJ
city0NN
measure0NN
approximately0RB
cover0NN
carbon0.0625160381065121NN
dioxide0NN
emission0.0625160381065121NN
energy0.494617163780726NN
consumption0NN
building0NN
land0.0325677023224526NN
transport0NN
water0NN
sanitation0NN
waste0.0625160381065121NN
management0NN
air0NN
quality0NN
environmental0JJ
governance0NN

Label: 13, with a score of: 0.161330291493278
WordScorePoS
green0.0335649154577099JJ
city0NN
measure0.0710409787438328NN
approximately0RB
cover0NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
energy0.0123065238088614NN
consumption0NN
building0NN
land0NN
transport0NN
water0NN
sanitation0NN
waste0NN
management0.0171695137813101NN
air0NN
quality0NN
environmental0JJ
governance0NN

Label: 8, with a score of: 0.0914443965820209
WordScorePoS
green0JJ
city0NN
measure0.0347609334428716NN
approximately0RB
cover0NN
carbon0NN
dioxide0NN
emission0NN
energy0NN
consumption0.147812295323721NN
building0NN
land0NN
transport0NN
water0NN
sanitation0NN
waste0NN
management0NN
air0NN
quality0.030144637392549NN
environmental0.0295821503613099JJ
governance0NN

Label: 17, with a score of: 0.0736232281510887
WordScorePoS
green0JJ
city0NN
measure0NN
approximately0RB
cover0NN
carbon0NN
dioxide0NN
emission0NN
energy0.0109843026967392NN
consumption0NN
building0.149940347231585NN
land0NN
transport0.0249900578719309NN
water0NN
sanitation0NN
waste0NN
management0NN
air0NN
quality0.00916459792075908NN
environmental0JJ
governance0NN

About half of the indicators in each Index are quantitative and usually these make use of data from official public sources for example carbon dioxide emissions per capita water consumption per capita recycling rates or air pollutant concentrations

Label: 12, with a score of: 0.629254628255534
WordScorePoS
datum0NN
public0.028867295332595NN
source0NN
carbon0NN
dioxide0NN
emission0.0554128417527413NN
capita0.0332151194779746NN
water0.186896403054502NN
consumption0.332151194779746NN
recycle0.0819583881728876VB
rate0NN
air0.0554128417527413NN
concentration0NN

Label: 6, with a score of: 0.562103238502967
WordScorePoS
datum0NN
public0NN
source0.0613562156822703NN
carbon0NN
dioxide0NN
emission0NN
capita0NN
water0.556409482163189NN
consumption0NN
recycle0.103230094596636VB
rate0NN
air0NN
concentration0NN

Label: 13, with a score of: 0.281072295944214
WordScorePoS
datum0NN
public0NN
source0.0123065238088614NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
capita0NN
water0NN
consumption0NN
recycle0VB
rate0NN
air0NN
concentration0.0548233071065585NN

Label: 8, with a score of: 0.192062539899619
WordScorePoS
datum0NN
public0NN
source0NN
carbon0NN
dioxide0NN
emission0NN
capita0.049270765107907NN
water0NN
consumption0.147812295323721NN
recycle0VB
rate0NN
air0NN
concentration0NN

Label: 11, with a score of: 0.308226470616603
WordScorePoS
datum0NN
public0.0583751976558017NN
source0NN
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN
capita0.04477822029736NN
water0.0458109463416786NN
consumption0.08955644059472NN
recycle0VB
rate0NN
air0.0373517915079663NN
concentration0NN

The remainder are qualitative assessments of the city environmental policies for example commitments to sourcing more renewable energy traffic congestion reduction policies or air quality codes

Label: 11, with a score of: 0.692681196552856
WordScorePoS
city0.607697021770652NN
environmental0.0537696560159885JJ
policy0.0136980135278128NN
commitment0NN
source0NN
renewable0JJ
energy0.0492535935240667NN
congestion0.0731385760866977NN
reduction0.0268848280079943NN
air0.0373517915079663NN
quality0.0136980135278128NN

Label: 7, with a score of: 0.530283141601097
WordScorePoS
city0NN
environmental0JJ
policy0NN
commitment0NN
source0.0274787313211514NN
renewable0.158624482267508JJ
energy0.494617163780726NN
congestion0NN
reduction0NN
air0NN
quality0NN

Label: 12, with a score of: 0.275409750546589
WordScorePoS
city0NN
environmental0.0598270388037045JJ
policy0.0203215381458133NN
commitment0NN
source0NN
renewable0.0234335422829798JJ
energy0.133960988253756NN
congestion0NN
reduction0.0199423462679015NN
air0.0554128417527413NN
quality0.0203215381458133NN

The specific indicators differ slightly by geography taking into account data availability and the unique challenges in each region

Label: 1, with a score of: 0.596679292282055
WordScorePoS
slightly0RB
account0NN
datum0NN
availability0NN
challenge0NN
region0.184259548107002NN

Label: 3, with a score of: 0.404256698840153
WordScorePoS
slightly0RB
account0NN
datum0NN
availability0NN
challenge0NN
region0.124837844401518NN

Label: 17, with a score of: 0.397839522782999
WordScorePoS
slightly0RB
account0NN
datum0.0739231123597559NN
availability0.048933054487825NN
challenge0NN
region0NN

Label: 11, with a score of: 0.369914273482765
WordScorePoS
slightly0RB
account0.0194583992186006NN
datum0NN
availability0NN
challenge0.0947742174515357NN
region0NN

Measuring quantitative and qualitative indicators together means the indices are based on current environmental performance as well as future intentions to be greener

Label: 1, with a score of: 0.73863554111607
WordScorePoS
measure0.103895385378356NN
means0.120266261535276NNS
base0.0442083771593746NN
current0JJ
environmental0.0442083771593746JJ
performance0NN
future0NN

Label: 14, with a score of: 0.34399206619872
WordScorePoS
measure0NN
means0NNS
base0.058665107245718NN
current0.0325700316331562JJ
environmental0JJ
performance0NN
future0.0543366405761884NN

Label: 12, with a score of: 0.332458408586143
WordScorePoS
measure0NN
means0NNS
base0.0199423462679015NN
current0.0332151194779746JJ
environmental0.0598270388037045JJ
performance0NN
future0.0277064208763707NN

Each city received an overall index ranking and ranking for each individual category

Label: 11, with a score of: 0.983149889676773
WordScorePoS
city0.607697021770652NN
receive0VB
individual0JJ

The results URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS European Green City Index Methodology Note The European Green City Index analyzed cities on the basis of quantitative and qualitative indicators in eight categories

Label: 11, with a score of: 0.990337492689942
WordScorePoS
result0NN
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
green0.04477822029736JJ
city0.607697021770652NN
basis0NN

This basic methodology was adapted for the other indices taking into account data availability and regional environmental challenges

Label: 11, with a score of: 0.60555100288907
WordScorePoS
basic0.0583751976558017JJ
account0.0194583992186006NN
datum0NN
availability0NN
regional0.0315914058171786JJ
environmental0.0537696560159885JJ
challenge0.0947742174515357NN

Label: 17, with a score of: 0.387619022385062
WordScorePoS
basic0JJ
account0NN
datum0.0739231123597559NN
availability0.048933054487825NN
regional0.0422721924574123JJ
environmental0JJ
challenge0NN

Label: 1, with a score of: 0.375931410161768
WordScorePoS
basic0.0639932865717261JJ
account0NN
datum0NN
availability0NN
regional0.0519476926891779JJ
environmental0.0442083771593746JJ
challenge0NN

Label: 9, with a score of: 0.370125104113512
WordScorePoS
basic0.0480507272610562JJ
account0.0160169090870187NN
datum0.0454743001434393NN
availability0NN
regional0.0260040237236563JJ
environmental0.0221298700466866JJ
challenge0NN

Label: 12, with a score of: 0.309843085088058
WordScorePoS
basic0.0144336476662975JJ
account0.0577345906651901NN
datum0NN
availability0NN
regional0JJ
environmental0.0598270388037045JJ
challenge0NN

were presented numerically for the European index and the U

Label: 0, with a score of: 1

and Canada index or in five performance bands from well above average to well below average for the Asian Latin American and African indices

Label: 10, with a score of: 0.733880754303423
WordScorePoS
performance0.064698038783335NN
average0.146608885563018NN
african0IN

Label: 13, with a score of: 0.679278321796772
WordScorePoS
performance0NN
average0.1656430212089NN
african0IN

Bands were used in regions where levels of data quality and comparability did not allow for detailed numerical ranking

Label: 1, with a score of: 0.627210000933893
WordScorePoS
region0.184259548107002NN
level0.0113874611101829NN
datum0NN
quality0NN

Label: 3, with a score of: 0.449130993132331
WordScorePoS
region0.124837844401518NN
level0NN
datum0NN
quality0.0152605842304358NN

Label: 4, with a score of: 0.318912582364061
WordScorePoS
region0NN
level0.0231525547935232NN
datum0NN
quality0.076326555629389NN

Label: 17, with a score of: 0.310924756878935
WordScorePoS
region0NN
level0.0138997399903163NN
datum0.0739231123597559NN
quality0.00916459792075908NN

The Index Leaders Wealthier and Good with Governance The leading cities across the regional Indices had several factors in common

Label: 11, with a score of: 0.956931190934427
WordScorePoS
wealthier0JJR
governance0NN
lead0NN
city0.607697021770652NN
regional0.0315914058171786JJ
common0.055245183797332JJ

Wealth was clear driver cities with more money can invest in infrastructure and set aside more generous budgets for environmental oversight

Label: 11, with a score of: 0.88271198529595
WordScorePoS
city0.607697021770652NN
money0NN
invest0VB
infrastructure0.0194583992186006NN
set0NN
environmental0.0537696560159885JJ
oversight0NN

Label: 9, with a score of: 0.277848649336615
WordScorePoS
city0NN
money0NN
invest0VB
infrastructure0.192202909044225NN
set0NN
environmental0.0221298700466866JJ
oversight0NN

A second key factor was governance the city commitment to robust and innovative environmental policies across all categories from energy to air quality

Label: 11, with a score of: 0.734888243296982
WordScorePoS
key0NN
governance0NN
city0.607697021770652NN
commitment0NN
environmental0.0537696560159885JJ
policy0.0136980135278128NN
energy0.0492535935240667NN
air0.0373517915079663NN
quality0.0136980135278128NN

Label: 7, with a score of: 0.527978707781132
WordScorePoS
key0.0625160381065121NN
governance0NN
city0NN
commitment0NN
environmental0JJ
policy0NN
energy0.494617163780726NN
air0NN
quality0NN

Label: 12, with a score of: 0.27467656042517
WordScorePoS
key0NN
governance0NN
city0NN
commitment0NN
environmental0.0598270388037045JJ
policy0.0203215381458133NN
energy0.133960988253756NN
air0.0554128417527413NN
quality0.0203215381458133NN

Another driver was consistency

Label: 0, with a score of: 1

Cities doing very well overall didn necessarily place number one for each of the eight or nine categories but were usually near the top across the board

Label: 11, with a score of: 0.997380969185671
WordScorePoS
city0.607697021770652NN

This suggests that successful cities are following holistic approach to environmental management see key lessons below

Label: 11, with a score of: 0.954906655915165
WordScorePoS
suggest0VB
successful0JJ
city0.607697021770652NN
holistic0.0731385760866977JJ
approach0NN
environmental0.0537696560159885JJ
management0.0687164195125178NN
key0NN

Autonomy and Unified Strategy Required Key Lessons from the Index Series In search of holistic approach

Label: 8, with a score of: 0.565605058198884
WordScorePoS
autonomy0NN
strategy0.0295821503613099NN
require0.104282800328615VB
key0NN
holistic0JJ
approach0NN

Label: 12, with a score of: 0.427247363248733
WordScorePoS
autonomy0NN
strategy0NN
require0.0468670845659596VB
key0NN
holistic0JJ
approach0.0542519672965169NN

Top performing cities take holistic approach to environmental problems recognizing that performance in one category such as transport is linked to success in others such as air quality

Label: 11, with a score of: 0.941851441396898
WordScorePoS
city0.607697021770652NN
holistic0.0731385760866977JJ
approach0NN
environmental0.0537696560159885JJ
recognize0VB
performance0NN
transport0.0747035830159326NN
air0.0373517915079663NN
quality0.0136980135278128NN

These cities often have dedicated environmental departments and structured communication between departments with different responsibilities for example water waste management and transport

Label: 11, with a score of: 0.736147439119173
WordScorePoS
city0.607697021770652NN
dedicate0VB
environmental0.0537696560159885JJ
structure0NN
communication0NN
responsibility0NN
water0.0458109463416786NN
waste0.0373517915079663NN
management0.0687164195125178NN
transport0.0747035830159326NN

Label: 6, with a score of: 0.496714545059685
WordScorePoS
city0NN
dedicate0VB
environmental0JJ
structure0NN
communication0NN
responsibility0NN
water0.556409482163189NN
waste0NN
management0.0428007293971684NN
transport0NN

Label: 12, with a score of: 0.370492941963024
WordScorePoS
city0NN
dedicate0VB
environmental0.0598270388037045JJ
structure0NN
communication0NN
responsibility0NN
water0.186896403054502NN
waste0.138532104381853NN
management0.0339811641917276NN
transport0.0277064208763707NN

One of the best examples from the series is Curitiba

Label: 0, with a score of: 1

As early as the faced with rapid population growth city officials implemented proposals to reduce urban sprawl create pedestrian areas and provide low cost rapid transit

Label: 11, with a score of: 0.854727170438801
WordScorePoS
face0NN
rapid0.0373517915079663JJ
population0.0194583992186006NN
growth0NN
city0.607697021770652NN
implement0.0273960270556257VB
reduce0.0200483235258943VB
urban0.27622591898666JJ
create0.0315914058171786VB
provide0.0269743071558856VB
cost0NN

Label: 8, with a score of: 0.281127236392041
WordScorePoS
face0NN
rapid0JJ
population0.0428212738652355NN
growth0.208565600657229NN
city0NN
implement0.030144637392549VB
reduce0.0055149369084567VB
urban0JJ
create0.0695218668857431VB
provide0VB
cost0NN

By the the urban plan involved integrated initiatives that addressed issues such as the creation of green areas waste recycling and management and sanitation

Label: 11, with a score of: 0.582350551028767
WordScorePoS
urban0.27622591898666JJ
plan0.0583751976558017NN
involve0VB
integrate0.0537696560159885VB
initiative0NN
address0NN
issue0NN
creation0NN
green0.04477822029736JJ
waste0.0373517915079663NN
recycle0VB
management0.0687164195125178NN
sanitation0NN

Label: 6, with a score of: 0.546299066122821
WordScorePoS
urban0JJ
plan0NN
involve0VB
integrate0.0251182378961834VB
initiative0NN
address0NN
issue0NN
creation0NN
green0JJ
waste0NN
recycle0.103230094596636VB
management0.0428007293971684NN
sanitation0.334686906581616NN

Label: 12, with a score of: 0.436339831976115
WordScorePoS
urban0JJ
plan0.0144336476662975NN
involve0.0819583881728876VB
integrate0.0199423462679015VB
initiative0NN
address0NN
issue0NN
creation0NN
green0.0332151194779746JJ
waste0.138532104381853NN
recycle0.0819583881728876VB
management0.0339811641917276NN
sanitation0NN

Label: 8, with a score of: 0.205776497961329
WordScorePoS
urban0JJ
plan0NN
involve0VB
integrate0.0295821503613099VB
initiative0NN
address0NN
issue0NN
creation0.160952962344818NN
green0JJ
waste0NN
recycle0VB
management0NN
sanitation0NN

ANNEX Boston New York San Francisco and Seattle all scored well in the U

Label: 0, with a score of: 1

and Canada Index largely because they have integrated their environmental programs into wider development strategies that simultaneously revitalize their economies and make urban areas more livable

Label: 11, with a score of: 0.771389556226015
WordScorePoS
integrate0.0537696560159885VB
environmental0.0537696560159885JJ
development0.0100241617629471NN
strategy0NN
revitalize0VB
economy0NN
urban0.27622591898666JJ

Label: 8, with a score of: 0.345665271274322
WordScorePoS
integrate0.0295821503613099VB
environmental0.0295821503613099JJ
development0.0055149369084567NN
strategy0.0295821503613099NN
revitalize0VB
economy0.0821985033584295NN
urban0JJ

These cities stand out as examples pointing the way forward

Label: 11, with a score of: 0.994179944727542
WordScorePoS
city0.607697021770652NN
stand0VB

In many cities around the world different departments manage different aspects of sustainable urban development with no one setting the overall strategy

Label: 11, with a score of: 0.947520069131398
WordScorePoS
city0.607697021770652NN
world0.00626002771693937NN
manage0VB
aspect0NN
sustainable0.00782503464617421JJ
urban0.27622591898666JJ
development0.0100241617629471NN
set0NN
strategy0NN

Cities need more autonomy

Label: 11, with a score of: 0.995158755405856
WordScorePoS
city0.607697021770652NN
autonomy0NN

One important driver for urban sustainability is autonomy at the municipal level

Label: 11, with a score of: 0.944240682757419
WordScorePoS
urban0.27622591898666JJ
sustainability0NN
autonomy0NN
municipal0.0731385760866977JJ
level0.00692515656684925NN

Top performing cities such as Singapore and Hong Kong have the authority to set their own environmental policies and the funding to implement them

Label: 11, with a score of: 0.909123518308643
WordScorePoS
city0.607697021770652NN
set0NN
environmental0.0537696560159885JJ
policy0.0136980135278128NN
implement0.0273960270556257VB

This autonomy allows local officials to set their own priorities and respond more effectively to local needs without depending on more removed national government which may have competing priorities

Label: 17, with a score of: 0.526813649966115
WordScorePoS
autonomy0NN
local0.0130185595639838JJ
set0.0978661089756501NN
priority0NN
depend0VB
remove0VB
national0.0134132459226336JJ
government0.048933054487825NN

Unfortunately weak local governments are widespread problem especially in the developing world

Label: 17, with a score of: 0.71896147414915
WordScorePoS
local0.0130185595639838JJ
government0.048933054487825NN
develop0VB
world0.00418824502414673NN

In Africa decentralization of power from the national to the local level is crucial for effective planning but there is trend towards national governments taking even more authority over decisions about cities

Label: 11, with a score of: 0.896692133189941
WordScorePoS
africa0IN
power0NN
national0.00501208088147356JJ
local0.0194583992186006JJ
level0.00692515656684925NN
crucial0JJ
plan0.0583751976558017NN
government0NN
decision0NN
city0.607697021770652NN

Strong local governments were one of the main reasons for the relatively high ranking of South African cities as well as Accra

Label: 11, with a score of: 0.982890183776987
WordScorePoS
strong0JJ
local0.0194583992186006JJ
government0NN
south0RB
african0IN
city0.607697021770652NN

The Tipping Point Income and Environmental Performance

Label: 10, with a score of: 0.789509089398823
WordScorePoS
income0.166475342664294NN
environmental0.0237821918091848JJ
performance0.064698038783335NN

Label: 9, with a score of: 0.411171248211345
WordScorePoS
income0.110649350233433NN
environmental0.0221298700466866JJ
performance0NN

In most regions there was connection between city wealth as measured by GDP per capita and its performance in the Green City Index the higher the income the better the result

Label: 11, with a score of: 0.926523455929166
WordScorePoS
region0NN
city0.607697021770652NN
measure0NN
gdp0NN
capita0.04477822029736NN
performance0NN
green0.04477822029736JJ
income0NN
result0NN

Label: 1, with a score of: 0.235980144033202
WordScorePoS
region0.184259548107002NN
city0NN
measure0.103895385378356NN
gdp0NN
capita0NN
performance0NN
green0JJ
income0.0442083771593746NN
result0NN

Label: 10, with a score of: 0.164134626486994
WordScorePoS
region0NN
city0NN
measure0NN
gdp0NN
capita0NN
performance0.064698038783335NN
green0JJ
income0.166475342664294NN
result0NN

Especially in developing cities rising incomes initially cause higher levels of resource consumption waste and pollution

Label: 11, with a score of: 0.761713497544511
WordScorePoS
develop0VB
city0.607697021770652NN
rise0.0268848280079943NN
income0NN
level0.00692515656684925NN
resource0.0449504361447405NN
consumption0.08955644059472NN
waste0.0373517915079663NN
pollution0.0315914058171786NN

Label: 12, with a score of: 0.487640659223374
WordScorePoS
develop0VB
city0NN
rise0NN
income0NN
level0.00513687014008325NN
resource0.0416785799329393NN
consumption0.332151194779746NN
waste0.138532104381853NN
pollution0.0234335422829798NN

Label: 10, with a score of: 0.171513719901194
WordScorePoS
develop0VB
city0NN
rise0.0237821918091848NN
income0.166475342664294NN
level0NN
resource0NN
consumption0NN
waste0NN
pollution0NN

Label: 8, with a score of: 0.151266233867007
WordScorePoS
develop0VB
city0NN
rise0NN
income0NN
level0.00761994916892276NN
resource0.0123650647908625NN
consumption0.147812295323721NN
waste0NN
pollution0NN

The Asian Index shows that only when GDP per capita rises above approximately per person is there boost for the environment tipping point at which wealthy residents start to consume relatively less water generate less waste and produce less carbon

Label: 6, with a score of: 0.610788555265606
WordScorePoS
gdp0NN
capita0NN
rise0.0251182378961834NN
approximately0RB
person0NN
environment0NN
consume0VB
water0.556409482163189NN
generate0VB
waste0NN
produce0VB
carbon0NN

Label: 12, with a score of: 0.538400280831842
WordScorePoS
gdp0NN
capita0.0332151194779746NN
rise0NN
approximately0RB
person0NN
environment0.079769385071606NN
consume0.0409791940864438VB
water0.186896403054502NN
generate0VB
waste0.138532104381853NN
produce0.0332151194779746VB
carbon0NN

Label: 11, with a score of: 0.343035395256266
WordScorePoS
gdp0NN
capita0.04477822029736NN
rise0.0268848280079943NN
approximately0RB
person0.107539312031977NN
environment0.0268848280079943NN
consume0VB
water0.0458109463416786NN
generate0VB
waste0.0373517915079663NN
produce0VB
carbon0.0373517915079663NN

Osaka Singapore Taipei and Tokyo all showed evidence of this phenomenon in the Asian Index

Label: 10, with a score of: 1
WordScorePoS
evidence0.12939607756667NN

Generally speaking as city residents reach certain level of wealth they tend to acquire growing awareness of environmental issues

Label: 11, with a score of: 0.894972250606425
WordScorePoS
city0.607697021770652NN
reach0NN
level0.00692515656684925NN
acquire0NN
grow0.0229054731708393VB
awareness0NN
environmental0.0537696560159885JJ
issue0NN

They begin to support policies that limit consumption in favor of promoting urban sustainability and improved livability

Label: 11, with a score of: 0.598378451305084
WordScorePoS
support0.0224752180723703NN
policy0.0136980135278128NN
limit0NN
consumption0.08955644059472NN
promote0VB
urban0.27622591898666JJ
sustainability0NN
improve0.0273960270556257VB

Label: 12, with a score of: 0.580688843069866
WordScorePoS
support0.00833571598658786NN
policy0.0203215381458133NN
limit0NN
consumption0.332151194779746NN
promote0.0148712355431627VB
urban0JJ
sustainability0.0409791940864438NN
improve0VB

Label: 8, with a score of: 0.362922980346413
WordScorePoS
support0.024730129581725NN
policy0.0452169560888236NN
limit0NN
consumption0.147812295323721NN
promote0.0275746845422835VB
urban0JJ
sustainability0NN
improve0.0150723186962745VB

Not only for the rich What developing cities can achieve

Label: 11, with a score of: 0.991721628646166
WordScorePoS
develop0VB
city0.607697021770652NN
achieve0VB

Although wealth undoubtedly plays role in environmental performance the Green City Top Cities in Each Region European Green City Index US Canada Green City Index Latin American Green City Index Asian Green City Index African Green City Index Top five cities by rank of total Top five cities by rank of total All cities placing above average of total All cities placing above average of total All cities placing above average of total st

Label: 11, with a score of: 0.985948182167282
WordScorePoS
environmental0.0537696560159885JJ
performance0NN
green0.04477822029736JJ
city0.607697021770652NN
region0NN
african0IN
total0NN
average0NN
st0IN

Label: 13, with a score of: 0.0904167697595925
WordScorePoS
environmental0JJ
performance0NN
green0.0335649154577099JJ
city0NN
region0NN
african0IN
total0NN
average0.1656430212089NN
st0IN

Label: 10, with a score of: 0.0684040281527808
WordScorePoS
environmental0.0237821918091848JJ
performance0.064698038783335NN
green0JJ
city0NN
region0NN
african0IN
total0NN
average0.146608885563018NN
st0IN

Label: 1, with a score of: 0.0295815306749905
WordScorePoS
environmental0.0442083771593746JJ
performance0NN
green0JJ
city0NN
region0.184259548107002NN
african0IN
total0NN
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st0IN

Copenhagen st

Label: 0, with a score of: 1

San Francisco nd

Label: 0, with a score of: 1

Stockholm nd

Label: 0, with a score of: 1

Vancouver rd

Label: 0, with a score of: 1

Oslo rd

Label: 0, with a score of: 1

New York City th

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN

Vienna th

Label: 0, with a score of: 1

Seattle th

Label: 0, with a score of: 1

Amsterdam th

Label: 0, with a score of: 1

Denver Well above average Curitiba Well above average Singapore Above average Belo Horizonte Bogotá Brasília Rio de Janeiro São Paulo Above average Hong Kong Osaka Seoul Taipei Tokyo Yokohama Well above average none Above average Accra Cape Town Casablanca Durban Johannesburg Tunis Source Green Cities Index data

Label: 13, with a score of: 0.68528398844177
WordScorePoS
average0.1656430212089NN
source0.0123065238088614NN
green0.0335649154577099JJ
city0NN
datum0NN

Label: 10, with a score of: 0.579778002327485
WordScorePoS
average0.146608885563018NN
source0NN
green0JJ
city0NN
datum0NN

Label: 11, with a score of: 0.430045322016231
WordScorePoS
average0NN
source0NN
green0.04477822029736JJ
city0.607697021770652NN
datum0NN

Note Cities in Europe the United States and Canada were ranked numerically

Label: 11, with a score of: 0.986511716312676
WordScorePoS
city0.607697021770652NN
unite0VB

Cities in Asia Latin America and Africa were ranked in five bands from well above average to well below average

Label: 11, with a score of: 0.807066019678702
WordScorePoS
city0.607697021770652NN
asia0IN
africa0IN
average0NN

Label: 13, with a score of: 0.439972055236022
WordScorePoS
city0NN
asia0IN
africa0IN
average0.1656430212089NN

Label: 10, with a score of: 0.389414611169614
WordScorePoS
city0NN
asia0IN
africa0IN
average0.146608885563018NN

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Index series shows that low cost actions can have big benefits

Label: 11, with a score of: 0.733528479243393
WordScorePoS
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
cost0NN
action0NN

Label: 10, with a score of: 0.377231828428711
WordScorePoS
urban0.0488696285210061JJ
development0.0177346522508396NN
knowledge0NN
cost0.0660824365173543NN
action0.0145231653596925NN

Delhi in particular shows that those less well off can adopt policies and shape attitudes towards sustainability

Label: 17, with a score of: 0.570240316172482
WordScorePoS
adopt0.0153248097886055VB
policy0.0641521854453136NN
sustainability0.036961556179878NN

Label: 10, with a score of: 0.51462556209971
WordScorePoS
adopt0.0202620733250388VB
policy0.0848203862485078NN
sustainability0NN

Label: 12, with a score of: 0.383419952360699
WordScorePoS
adopt0.0169905820958638VB
policy0.0203215381458133NN
sustainability0.0409791940864438NN

The city had one of the lowest levels of GDP per capita in the Asian Index at an estimated

Label: 11, with a score of: 0.96766703067937
WordScorePoS
city0.607697021770652NN
level0.00692515656684925NN
gdp0NN
capita0.04477822029736NN
estimate0NN

Yet it still achieved an average overall rating with strong result in the waste category where it ranked above average

Label: 10, with a score of: 0.677163883167724
WordScorePoS
achieve0.018377882320743VB
average0.146608885563018NN
strong0.0488696285210061JJ
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waste0NN

Label: 13, with a score of: 0.622348265270532
WordScorePoS
achieve0VB
average0.1656430212089NN
strong0JJ
result0NN
waste0NN

Label: 12, with a score of: 0.28919413811977
WordScorePoS
achieve0.0154106104202498VB
average0NN
strong0JJ
result0NN
waste0.138532104381853NN

This is in part because of what has been called Delhi traditional culture of careful consumption tendency to reuse and recycle as much as possible

Label: 12, with a score of: 0.827087204394582
WordScorePoS
traditional0JJ
culture0.0277064208763707NN
careful0JJ
consumption0.332151194779746NN
reuse0.0409791940864438NN
recycle0.0819583881728876VB

Label: 8, with a score of: 0.323628577838251
WordScorePoS
traditional0JJ
culture0.0410992516792148NN
careful0JJ
consumption0.147812295323721NN
reuse0NN
recycle0VB

Label: 6, with a score of: 0.353691547546304
WordScorePoS
traditional0JJ
culture0NN
careful0JJ
consumption0NN
reuse0.103230094596636NN
recycle0.103230094596636VB

Building on this however Delhi has introduced advanced policies including one of the more robust strategies in the index to reduce reuse and recycle waste demonstrating just how much can be achieved with limited resources and popular support

Label: 12, with a score of: 0.488449359475018
WordScorePoS
building0NN
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advance0.0277064208763707NN
policy0.0203215381458133NN
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reduce0.022306853314744VB
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limited0JJ
resource0.0416785799329393NN
support0.00833571598658786NN

Label: 17, with a score of: 0.39689043663309
WordScorePoS
building0.149940347231585NN
introduce0VB
advance0NN
policy0.0641521854453136NN
include0VB
strategy0.017987180284335NN
reduce0.00335331148065841VB
reuse0NN
recycle0VB
waste0NN
achieve0.00463324666343875VB
limited0JJ
resource0.0375923735942499NN
support0.0451108483130998NN

Label: 1, with a score of: 0.405520528847837
WordScorePoS
building0NN
introduce0VB
advance0NN
policy0.0450489732120807NN
include0VB
strategy0.0442083771593746NN
reduce0.0164833460638534VB
reuse0NN
recycle0VB
waste0NN
achieve0.0113874611101829VB
limited0.120266261535276JJ
resource0.0739147683583536NN
support0.0184786920895884NN

Label: 6, with a score of: 0.370897670883877
WordScorePoS
building0.0348974218717993NN
introduce0VB
advance0NN
policy0NN
include0VB
strategy0NN
reduce0.00936547854413134VB
reuse0.103230094596636NN
recycle0.103230094596636VB
waste0NN
achieve0.0194103228106253VB
limited0JJ
resource0.0104991907358033NN
support0.0209983814716066NN

Label: 11, with a score of: 0.308122277061378
WordScorePoS
building0.0747035830159326NN
introduce0VB
advance0.0373517915079663NN
policy0.0136980135278128NN
include0VB
strategy0NN
reduce0.0200483235258943VB
reuse0NN
recycle0VB
waste0.0373517915079663NN
achieve0VB
limited0JJ
resource0.0449504361447405NN
support0.0224752180723703NN

Indeed public engagement in policies is often prerequisite for successful policies in any city developed or developing

Label: 11, with a score of: 0.885946461019591
WordScorePoS
public0.0583751976558017NN
policy0.0136980135278128NN
successful0JJ
city0.607697021770652NN
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In the European index there was correlation between levels of civic engagement and environmental performance

Label: 10, with a score of: 0.534758425129633
WordScorePoS
level0NN
environmental0.0237821918091848JJ
performance0.064698038783335NN

Low income cities can also look to international agencies to finance environmental goals

Label: 11, with a score of: 0.904971293876566
WordScorePoS
income0NN
city0.607697021770652NN
international0JJ
agency0NN
environmental0.0537696560159885JJ
goal0NN

Label: 10, with a score of: 0.26811741176788
WordScorePoS
income0.166475342664294NN
city0NN
international0.00571634317201755JJ
agency0NN
environmental0.0237821918091848JJ
goal0NN

One example is Vilnius ranked th in the European index making it the best performing city in Eastern Europe and among the low income cities in the European Index

Label: 11, with a score of: 0.977379220198758
WordScorePoS
city0.607697021770652NN
eastern0JJ
income0NN

Label: 10, with a score of: 0.133873899958782
WordScorePoS
city0NN
eastern0JJ
income0.166475342664294NN

The city took advantage of funding from the World Health Organization Healthy Cities project to promote the use of cycling and public transport

Label: 11, with a score of: 0.94995989249253
WordScorePoS
city0.607697021770652NN
world0.00626002771693937NN
health0.0229054731708393NN
organization0NN
healthy0JJ
project0NN
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public0.0583751976558017NN
transport0.0747035830159326NN

Label: 3, with a score of: 0.207872436127183
WordScorePoS
city0NN
world0.00087176582269799NN
health0.191387734041787NN
organization0.0175975629039954NN
healthy0.0615471563780609JJ
project0NN
promote0.00837573444202498VB
public0.021678073222951NN
transport0NN

It also drew on European Union funds to improve its water supply and treatment network

Label: 6, with a score of: 0.899232059278208
WordScorePoS
fund0NN
improve0.0511916925627245VB
water0.556409482163189NN
supply0.0590310971343993NN
treatment0.0348974218717993NN

Label: 12, with a score of: 0.329679294984522
WordScorePoS
fund0NN
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water0.186896403054502NN
supply0.0703006268489394NN
treatment0NN

Conclusion Apples to Apples the Challenge of Collecting Comparable Data Worldwide Data collection was challenge to some extent in all of the regions covered by the Green City Index series

Label: 11, with a score of: 0.898116251738724
WordScorePoS
conclusion0NN
challenge0.0947742174515357NN
datum0NN
worldwide0RB
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green0.04477822029736JJ
city0.607697021770652NN

Label: 1, with a score of: 0.196534253393227
WordScorePoS
conclusion0NN
challenge0NN
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worldwide0RB
collection0NN
extent0NN
region0.184259548107002NN
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Many cities diligently collect key environmental data and update it regularly

Label: 11, with a score of: 0.967484896888052
WordScorePoS
city0.607697021770652NN
key0NN
environmental0.0537696560159885JJ
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The challenge comes when comparing information across cities

Label: 11, with a score of: 0.978937238618306
WordScorePoS
challenge0.0947742174515357NN
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information0NN
city0.607697021770652NN

For example in Europe one of the more accessible regions in terms of environmental metrics around one third of the cities in the Index did not measure the full amount of energy consumed in their city or the associated carbon dioxide emissions

Label: 11, with a score of: 0.825084353469385
WordScorePoS
accessible0.110490367594664JJ
region0NN
term0NN
environmental0.0537696560159885JJ
city0.607697021770652NN
measure0NN
full0JJ
amount0NN
energy0.0492535935240667NN
consume0VB
carbon0.0373517915079663NN
dioxide0NN
emission0.0373517915079663NN

Label: 7, with a score of: 0.381148675433655
WordScorePoS
accessible0JJ
region0NN
term0.0749457204978913NN
environmental0JJ
city0NN
measure0NN
full0JJ
amount0NN
energy0.494617163780726NN
consume0VB
carbon0.0625160381065121NN
dioxide0NN
emission0.0625160381065121NN

Label: 13, with a score of: 0.222095228561771
WordScorePoS
accessible0JJ
region0NN
term0NN
environmental0JJ
city0NN
measure0.0710409787438328NN
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amount0.0279982034978914NN
energy0.0123065238088614NN
consume0VB
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN

Label: 1, with a score of: 0.216082664551839
WordScorePoS
accessible0JJ
region0.184259548107002NN
term0NN
environmental0.0442083771593746JJ
city0NN
measure0.103895385378356NN
full0JJ
amount0.0614198493690006NN
energy0NN
consume0VB
carbon0NN
dioxide0NN
emission0NN

Label: 12, with a score of: 0.1592320026797
WordScorePoS
accessible0JJ
region0NN
term0NN
environmental0.0598270388037045JJ
city0NN
measure0NN
full0JJ
amount0NN
energy0.133960988253756NN
consume0.0409791940864438VB
carbon0NN
dioxide0NN
emission0.0554128417527413NN

Label: 17, with a score of: 0.0882243742396351
WordScorePoS
accessible0JJ
region0NN
term0.149793392961411NN
environmental0JJ
city0NN
measure0NN
full0JJ
amount0NN
energy0.0109843026967392NN
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carbon0NN
dioxide0NN
emission0NN

In many cases the Economist Intelligence Unit made statistical estimates extrapolating from partial data or national figures to fill data gaps

Label: 17, with a score of: 0.724326287301106
WordScorePoS
statistical0.048933054487825JJ
estimate0NN
datum0.0739231123597559NN
national0.0134132459226336JJ
gap0NN

Label: 9, with a score of: 0.611347234878797
WordScorePoS
statistical0JJ
estimate0.0221298700466866NN
datum0.0454743001434393NN
national0.00412562425683033JJ
gap0.0602029956716497NN

Overlapping jurisdictions within regions was challenge too data for energy transport or air quality may have been collected at the metropolitan level in some jurisdictions the municipal level in others or in some cities not at all

Label: 11, with a score of: 0.792476459892613
WordScorePoS
region0NN
challenge0.0947742174515357NN
datum0NN
energy0.0492535935240667NN
transport0.0747035830159326NN
air0.0373517915079663NN
quality0.0136980135278128NN
level0.00692515656684925NN
municipal0.0731385760866977JJ
city0.607697021770652NN

Label: 7, with a score of: 0.449859316612706
WordScorePoS
region0NN
challenge0.0528748274225028NN
datum0NN
energy0.494617163780726NN
transport0NN
air0NN
quality0NN
level0NN
municipal0JJ
city0NN

Label: 12, with a score of: 0.203508263402541
WordScorePoS
region0NN
challenge0NN
datum0NN
energy0.133960988253756NN
transport0.0277064208763707NN
air0.0554128417527413NN
quality0.0203215381458133NN
level0.00513687014008325NN
municipal0JJ
city0NN

Label: 1, with a score of: 0.170114607762675
WordScorePoS
region0.184259548107002NN
challenge0NN
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energy0NN
transport0NN
air0NN
quality0NN
level0.0113874611101829NN
municipal0JJ
city0NN

A related problem was that urban agglomerations which need to be integrated into municipal planning for sustainability policies to be effective often lacked single data source

Label: 11, with a score of: 0.758637265484686
WordScorePoS
related0.0112376090361851JJ
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municipal0.0731385760866977JJ
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sustainability0NN
policy0.0136980135278128NN
lack0.0229054731708393NN
single0JJ
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source0NN

In addition in developing cities acquiring data on informal settlements which have huge environmental impacts proved especially difficult

Label: 11, with a score of: 0.957014030906621
WordScorePoS
addition0NN
develop0VB
city0.607697021770652NN
acquire0NN
datum0NN
settlement0.146277152173395NN
environmental0.0537696560159885JJ
impact0.0194583992186006NN

Overall across the Green City Index regions there were very few instances in which one single data point carbon dioxide emissions per capita for example was measured and reported in the same way in each region

Label: 11, with a score of: 0.713154058888431
WordScorePoS
green0.04477822029736JJ
city0.607697021770652NN
region0NN
single0JJ
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carbon0.0373517915079663NN
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capita0.04477822029736NN
measure0NN
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Label: 1, with a score of: 0.436428823392437
WordScorePoS
green0JJ
city0NN
region0.184259548107002NN
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carbon0NN
dioxide0NN
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measure0.103895385378356NN
report0NN

Label: 13, with a score of: 0.367683228169323
WordScorePoS
green0.0335649154577099JJ
city0NN
region0NN
single0JJ
datum0NN
carbon0.0559964069957828NN
dioxide0.0414107553022249NN
emission0.19598742448524NN
capita0NN
measure0.0710409787438328NN
report0NN

This lack of comparability is call to action in itself

Label: 1, with a score of: 0.67006069987149
WordScorePoS
lack0.0753297582301286NN
action0.0269969049497485NN

Label: 9, with a score of: 0.458882423698276
WordScorePoS
lack0.0565629593551219NN
action0.0135141354781442NN

Establishing set of agreedupon global metrics for urban carbon emissions energy consumption air quality and other key environmental performance indicators would be major step towards providing policymakers with comprehensive assessment of their cities current environmental footprint

Label: 11, with a score of: 0.716849440860835
WordScorePoS
establish0VB
set0NN
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Label: 12, with a score of: 0.441038697967401
WordScorePoS
establish0VB
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carbon0NN
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current0.0332151194779746JJ

Label: 7, with a score of: 0.411794578102739
WordScorePoS
establish0VB
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global0.0251662980592747JJ
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carbon0.0625160381065121NN
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Label: 13, with a score of: 0.202917802751193
WordScorePoS
establish0VB
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carbon0.0559964069957828NN
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major0.0291713033871158JJ
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current0.0335649154577099JJ

Label: 8, with a score of: 0.156164643372448
WordScorePoS
establish0VB
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urban0JJ
carbon0NN
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consumption0.147812295323721NN
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environmental0.0295821503613099JJ
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city0NN
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More importantly consistent set of sustainability indicators would help reveal the most appropriate municipal policies and efficient investments to improve green performance

Label: 17, with a score of: 0.723321305159087
WordScorePoS
consistent0.036961556179878JJ
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municipal0JJ
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Label: 10, with a score of: 0.39762044668171
WordScorePoS
consistent0JJ
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Label: 11, with a score of: 0.347590768196755
WordScorePoS
consistent0JJ
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municipal0.0731385760866977JJ
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More information on the Green City Index www

Label: 11, with a score of: 0.981866666514492
WordScorePoS
information0NN
green0.04477822029736JJ
city0.607697021770652NN

siemens

Label: 0, with a score of: 1

com greencityindex ANNEX ANNEX Global City Indicators Facility GCIF The Global City Indicators Facility GCIF is positioned to be the definitive and authoritative compilation of validated self reported worldwide urban data

Label: 11, with a score of: 0.981343390938928
WordScorePoS
global0.00501208088147356JJ
city0.607697021770652NN
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worldwide0RB
urban0.27622591898666JJ
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For the first time database hosting globally comparative city data based upon globally standardized methodology provides platform for comparative global research

Label: 11, with a score of: 0.891610968443814
WordScorePoS
time0NN
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city0.607697021770652NN
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global0.00501208088147356JJ
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This Facility also provides solid base for evidencebased policy and management at the local level to build more sustainable cities

Label: 11, with a score of: 0.871835747097753
WordScorePoS
facility0NN
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management0.0687164195125178NN
local0.0194583992186006JJ
level0.00692515656684925NN
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city0.607697021770652NN

Headquartered in Toronto the GCIF is rapidly becoming global leader and centre of excellence on globally standardized city metrics

Label: 11, with a score of: 0.977510012671006
WordScorePoS
rapidly0RB
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As cities worldwide increasingly take centre stage in the sustainable economic development and prosperity of nations the need for globally comparable data and knowledge on cities has never been greater

Label: 11, with a score of: 0.940279218716579
WordScorePoS
city0.607697021770652NN
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sustainable0.00782503464617421JJ
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Label: 8, with a score of: 0.0984570768515992
WordScorePoS
city0NN
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sustainable0.00688808874408283JJ
economic0.128463821595706JJ
development0.0055149369084567NN
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The GCIF hosts standardized system of global city indicators with support from the World Bank the University of Toronto the Government of Ontario Canada and worldwide network of participating cities

Label: 11, with a score of: 0.960309625572153
WordScorePoS
system0.0315914058171786NN
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worldwide0RB

Currently indicators across more than cities are collected annually

Label: 11, with a score of: 0.997380969185672
WordScorePoS
city0.607697021770652NN
annually0RB

GCIF member cities are representative of all regions of the world and the GCIF aims to increase membership to cities by cities by and cities by

Label: 11, with a score of: 0.988762769273562
WordScorePoS
city0.607697021770652NN
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increase0.00323105002784081NN

Label: 1, with a score of: 0.0757017896186315
WordScorePoS
city0NN
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The GCIF indicators are undergoing ISO review for standardization

Label: 5, with a score of: 0.698039924281796
WordScorePoS
undergo0VB
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Label: 9, with a score of: 0.610228222164062
WordScorePoS
undergo0.0602029956716497VB
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To date no city data conforms to standardized methodology that can ensure sound comparative urban research for global learning on sustainable cities

Label: 11, with a score of: 0.956106583815605
WordScorePoS
city0.607697021770652NN
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Label: 4, with a score of: 0.188707808144995
WordScorePoS
city0NN
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The GCIF provides cities with free web based system to enter city data track progress over time and facilitate capacity building and knowledge sharing

Label: 11, with a score of: 0.863245545965983
WordScorePoS
city0.607697021770652NN
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Label: 17, with a score of: 0.384472866278948
WordScorePoS
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Globally comparative data strengthens cities policy leverage and performance management through evidence based decision making

Label: 11, with a score of: 0.837570539645173
WordScorePoS
globally0RB
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Label: 10, with a score of: 0.37237813100201
WordScorePoS
globally0RB
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Citizens and businesses are empowered through transparent access to accurate performance information about their cities and other cities in comparative global framework

Label: 11, with a score of: 0.958662940735914
WordScorePoS
business0NN
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The GCIF also provides support for international development agencies provision of validated worldwide urban data

Label: 11, with a score of: 0.73227580461694
WordScorePoS
support0.0224752180723703NN
international0JJ
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agency0NN
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worldwide0RB
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Label: 17, with a score of: 0.409076613987111
WordScorePoS
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worldwide0RB
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The mission of the GCIF is to improve knowledge on cities worldwide through the collection organization and analysis of urban information thereby assisting cities globally in evidence based policy planning and management and comparative learning for sustainable cities

Label: 11, with a score of: 0.95309113343485
WordScorePoS
improve0.0273960270556257VB
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Label: 4, with a score of: 0.143966682086841
WordScorePoS
improve0.0305306222517556VB
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Label: 10, with a score of: 0.141983388727817
WordScorePoS
improve0.0121171980355011VB
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The GCIF strives to ' improve the capacity for researchers to undertake comparative analysis of cities globally ' help city leaders make informed evidence based decisions ' provide globally comparative city data for senior levels of government responsible for economic productivity and global competitiveness ' empower the business sector and citizens through access to accurate performance information about their cities and other cities in comparative global framework ' support international development agencies in the provision of validated worldwide urban data ' provide standardized tool for cities to make global comparisons and track performance over time and ' improve understanding of cities in key sectors including finance sustainability climate risk transport emergency services water waste management housing and all city services diversity and quality of life from the local to global scale URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS Website The GCIF website www

Label: 11, with a score of: 0.915354143111644
WordScorePoS
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Label: 12, with a score of: 0.164578928873928
WordScorePoS
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Label: 6, with a score of: 0.128684355528713
WordScorePoS
strive0VBP
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Label: 10, with a score of: 0.115703889502017
WordScorePoS
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Label: 13, with a score of: 0.0935476825232513
WordScorePoS
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Label: 8, with a score of: 0.0790852075005817
WordScorePoS
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cityindicators

Label: 0, with a score of: 1

org provides an uncomplicated relational database for cities to input manage and update indicators for their city

Label: 11, with a score of: 0.987064898324611
WordScorePoS
city0.607697021770652NN
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It provides member cities with tool to measure progress toward achieving performance goals access information about peer cities globally and share information as well as expertise

Label: 11, with a score of: 0.926618043871826
WordScorePoS
city0.607697021770652NN
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The website also increases transparency in terms of providing the business sector and the public at large as well as the international development community with accurate performance information about their cities and generates essential baseline urban data for the academic community

Label: 11, with a score of: 0.785737984978992
WordScorePoS
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Label: 17, with a score of: 0.315474607648455
WordScorePoS
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In addition the GCIF website is tool for senior levels of government responsible for economic productivity and global competitiveness

Label: 8, with a score of: 0.452433445123535
WordScorePoS
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Label: 10, with a score of: 0.411634856520372
WordScorePoS
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Label: 12, with a score of: 0.407031392273569
WordScorePoS
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Label: 9, with a score of: 0.365886490883994
WordScorePoS
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The GCIF online presence facilitates the dissemination of not only the indicators data but also research documents global reports policy briefs and other publications

Label: 17, with a score of: 0.641003476373849
WordScorePoS
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Label: 9, with a score of: 0.431894632561878
WordScorePoS
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Indicators The GCIF Indicators are structured around themes and measure range of city services and quality of life factors

Label: 11, with a score of: 0.903104210788471
WordScorePoS
structure0NN
measure0NN
range0NN
city0.607697021770652NN
service0.0269743071558856NN
quality0.0136980135278128NN
life0.0112376090361851NN

City performance relative to each of these themes is measured by suite of indicators that collectively tell story

Label: 11, with a score of: 0.968484458446691
WordScorePoS
city0.607697021770652NN
performance0NN
relative0.0731385760866977JJ
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Recognizing the differences in resources and capabilities between world cities the overall set of indicators has been divided into core indicators which all member cities are expected to report on and supporting indicators which all cities would be encouraged but not expected to report on

Label: 11, with a score of: 0.966458687656466
WordScorePoS
recognize0VB
resource0.0449504361447405NN
capability0NN
world0.00626002771693937NN
city0.607697021770652NN
set0NN
expect0VB
report0NN
support0.0224752180723703NN
encouraged0JJ

The current set of global city indicators was selected based on pilot phase with nine cities and from significant input from the current member cities ensuring that these indicators reflect city information needs interests and data availability

Label: 11, with a score of: 0.976286386611951
WordScorePoS
current0JJ
set0NN
global0.00501208088147356JJ
city0.607697021770652NN
base0NN
phase0NN
input0NN
ensure0.00501208088147356VB
information0NN
datum0NN
availability0NN

For more information please contact the Global City Indicators Facility at Global City Indicators Facility John H

Label: 11, with a score of: 0.972958760388211
WordScorePoS
information0NN
global0.00501208088147356JJ
city0.607697021770652NN
facility0NN

Daniels Faculty of Architecture Landscape Design University of Toronto Bloor Street West Suite Toronto Ontario S TEL FAX Email cityindicators daniels

Label: 0, with a score of: 1

utoronto

Label: 0, with a score of: 1

ca Web page www

Label: 0, with a score of: 1

cityindicators

Label: 0, with a score of: 1

org ANNEX ANNEX Summary of Measures Adopted in Selected Cities The following table summarizes measures adopted in sample of local Climate Action Plans divided by continent and type of instrument

Label: 11, with a score of: 0.696784540522144
WordScorePoS
measure0NN
adopt0.0229054731708393VB
city0.607697021770652NN
local0.0194583992186006JJ
climate0.0229054731708393NN
action0NN
plan0.0583751976558017NN
continent0NN
type0NN

Label: 13, with a score of: 0.54541099250387
WordScorePoS
measure0.0710409787438328NN
adopt0.0171695137813101VB
city0NN
local0.0145856516935579JJ
climate0.291881734282273NN
action0.0369195714265843NN
plan0.0291713033871158NN
continent0.0414107553022249NN
type0NN

The measures are listed in the Urban Transport Climate Action Plans of the cities that in Urban Transport and Climate Change Action Plans An Overview GIZ Source GIZ URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Sustainable Infrastructure Rating System Envision™ System Establishes Holistic Framework for Rating Sustainability of Infrastructure Projects The environment for infrastructure has become increasingly challenging as demands for energy and water resources climb

Label: 11, with a score of: 0.675414509598122
WordScorePoS
measure0NN
urban0.27622591898666JJ
transport0.0747035830159326NN
climate0.0229054731708393NN
action0NN
plan0.0583751976558017NN
city0.607697021770652NN
change0.0315914058171786NN
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development0.0100241617629471NN
knowledge0NN
sustainable0.00782503464617421JJ
infrastructure0.0194583992186006NN
system0.0315914058171786NN
establish0VB
holistic0.0731385760866977JJ
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sustainability0NN
project0NN
environment0.0268848280079943NN
challenge0.0947742174515357NN
demand0NN
energy0.0492535935240667NN
water0.0458109463416786NN
resource0.0449504361447405NN

Label: 13, with a score of: 0.386359308217345
WordScorePoS
measure0.0710409787438328NN
urban0JJ
transport0NN
climate0.291881734282273NN
action0.0369195714265843NN
plan0.0291713033871158NN
city0NN
change0.402565546215053NN
source0.0123065238088614NN
development0NN
knowledge0NN
sustainable0JJ
infrastructure0NN
system0NN
establish0VB
holistic0JJ
framework0.0291713033871158NN
sustainability0NN
project0.0335649154577099NN
environment0NN
challenge0.0236803262479443NN
demand0NN
energy0.0123065238088614NN
water0NN
resource0NN

Label: 7, with a score of: 0.331712515932894
WordScorePoS
measure0NN
urban0JJ
transport0NN
climate0.115011305411453NN
action0NN
plan0NN
city0NN
change0.105749654845006NN
source0.0274787313211514NN
development0NN
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sustainable0.0104774661535722JJ
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establish0VB
holistic0JJ
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environment0NN
challenge0.0528748274225028NN
demand0NN
energy0.494617163780726NN
water0NN
resource0NN

Label: 9, with a score of: 0.262549161531987
WordScorePoS
measure0NN
urban0JJ
transport0.030745604615229NN
climate0NN
action0.0135141354781442NN
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city0NN
change0NN
source0.0135141354781442NN
development0.0330049940546426NN
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sustainable0.00901749491165909JJ
infrastructure0.192202909044225NN
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establish0VB
holistic0JJ
framework0NN
sustainability0NN
project0NN
environment0.0221298700466866NN
challenge0NN
demand0.030745604615229NN
energy0.0540565419125769NN
water0.0377086395700813NN
resource0.0185001613201543NN

Label: 12, with a score of: 0.241330909744561
WordScorePoS
measure0NN
urban0JJ
transport0.0277064208763707NN
climate0NN
action0.0121782716594324NN
plan0.0144336476662975NN
city0NN
change0NN
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development0.022306853314744NN
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infrastructure0.028867295332595NN
system0NN
establish0VB
holistic0JJ
framework0.028867295332595NN
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project0.0664302389559493NN
environment0.079769385071606NN
challenge0NN
demand0NN
energy0.133960988253756NN
water0.186896403054502NN
resource0.0416785799329393NN

Label: 6, with a score of: 0.220257484420213
WordScorePoS
measure0NN
urban0JJ
transport0NN
climate0NN
action0NN
plan0NN
city0NN
change0NN
source0.0613562156822703NN
development0NN
knowledge0NN
sustainable0.00146217101876223JJ
infrastructure0.0181797964452808NN
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establish0VB
holistic0JJ
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project0.041835863322702NN
environment0NN
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energy0.0153390539205676NN
water0.556409482163189NN
resource0.0104991907358033NN

The professionals who design and build these projects face tall order in the years ahead satisfying ever growing demand while at the same time responsibly addressing the potential effects caused by climate change and the increasing demand for resources by integrating sustainable techniques in infrastructure design and construction

Label: 13, with a score of: 0.690096720461991
WordScorePoS
build0VB
project0.0335649154577099NN
grow0.0171695137813101VB
demand0NN
time0NN
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cause0NN
climate0.291881734282273NN
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increase0.0145316075945746NN
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integrate0.0201523636533764VB
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infrastructure0NN

Label: 9, with a score of: 0.363777129967523
WordScorePoS
build0.030745604615229VB
project0NN
grow0.0188543197850406VB
demand0.030745604615229NN
time0.0188543197850406NN
address0NN
potential0.0454743001434393NN
cause0NN
climate0NN
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increase0.0159575617614215NN
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sustainable0.00901749491165909JJ
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The civil infrastructure that best meets those challenges and that can serve as an example to others can now be rated by Envision™ rating system developed to gauge infrastructure sustainability

Label: 9, with a score of: 0.655177322535559
WordScorePoS
civil0JJ
infrastructure0.192202909044225NN
meet0VB
challenge0NN
serve0VB
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sustainability0NN

Label: 7, with a score of: 0.367659596326619
WordScorePoS
civil0JJ
infrastructure0.0651354046449052NN
meet0VB
challenge0.0528748274225028NN
serve0VB
rate0.0325677023224526NN
system0NN
develop0VB
sustainability0NN

Label: 2, with a score of: 0.358436489717854
WordScorePoS
civil0JJ
infrastructure0.0155217067874935NN
meet0.0881366997038154VB
challenge0NN
serve0VB
rate0.0155217067874935NN
system0.0756001353333673NN
develop0VB
sustainability0NN

Label: 17, with a score of: 0.310200428770985
WordScorePoS
civil0.0978661089756501JJ
infrastructure0.0130185595639838NN
meet0VB
challenge0NN
serve0VB
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sustainability0.036961556179878NN

Envision™ is the product of strategic alliance and collaboration of several organizations including the Institute for Sustainable Infrastructure ISI nonprofit organization co founded by the American Public Works Association the American Society of Civil Engineers and the American Council of Engineering Companies and the Zofnass Program for Sustainable Infrastructure at the Harvard University Graduate School of Design

Label: 9, with a score of: 0.551107301932917
WordScorePoS
product0.0442597400933733NN
organization0NN
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sustainable0.00901749491165909JJ
infrastructure0.192202909044225NN
public0.0160169090870187NN
society0.0221298700466866NN
civil0JJ
engineering0NN
company0NN
university0NN
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Label: 4, with a score of: 0.441686334174269
WordScorePoS
product0NN
organization0NN
include0VB
sustainable0.00872035848132902JJ
infrastructure0NN
public0NN
society0NN
civil0JJ
engineering0.0815069365350084NN
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university0.0815069365350084NN
school0.208127520578185NN

To meet the serious infrastructure challenges society currently faces the Envision™ rating system is designed to be used as an integrated educational and resource library as well as project assessment system

Label: 9, with a score of: 0.371876889478071
WordScorePoS
meet0VB
infrastructure0.192202909044225NN
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project0NN

Label: 2, with a score of: 0.464325219151776
WordScorePoS
meet0.0881366997038154VB
infrastructure0.0155217067874935NN
challenge0NN
society0NN
system0.0756001353333673NN
integrate0VB
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resource0.0358563662904843NN
project0NN

Label: 11, with a score of: 0.441039048185016
WordScorePoS
meet0VB
infrastructure0.0194583992186006NN
challenge0.0947742174515357NN
society0NN
system0.0315914058171786NN
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The assessment recognizes the need to stretch the traditional design boundaries

Label: 0, with a score of: 1

Infrastructure projects should be judged not only by how they are delivered but by how long they last accounting for durability flexibility and utility of the constructed works

Label: 9, with a score of: 0.725935590851127
WordScorePoS
infrastructure0.192202909044225NN
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Label: 7, with a score of: 0.48135573764174
WordScorePoS
infrastructure0.0651354046449052NN
project0NN
deliver0VB
accounting0.0924643738905717NN

Label: 12, with a score of: 0.392515166105738
WordScorePoS
infrastructure0.028867295332595NN
project0.0664302389559493NN
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The new sustainable infrastructure rating system will evaluate and grade infrastructure projects recognizing those that provide progress and contributions for sustainable future

Label: 9, with a score of: 0.707109661033195
WordScorePoS
sustainable0.00901749491165909JJ
infrastructure0.192202909044225NN
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progress0.0221298700466866NN
contribution0NN
future0NN

Label: 12, with a score of: 0.308261189416422
WordScorePoS
sustainable0.0162522423871414JJ
infrastructure0.028867295332595NN
system0NN
project0.0664302389559493NN
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contribution0NN
future0.0277064208763707NN

Its purpose is to foster necessary and dramatic improvement in the performance and resilience of physical infrastructure across all dimensions of sustainability economic social and environmental

Label: 1, with a score of: 0.611363360273869
WordScorePoS
foster0JJ
improvement0NN
performance0NN
resilience0.0614198493690006NN
infrastructure0NN
dimension0.181686110904276NN
sustainability0NN
economic0.0959899298575892JJ
social0.112994637345193JJ
environmental0.0442083771593746JJ

Label: 9, with a score of: 0.438745664986188
WordScorePoS
foster0.0454743001434393JJ
improvement0.0454743001434393NN
performance0NN
resilience0NN
infrastructure0.192202909044225NN
dimension0NN
sustainability0NN
economic0.0320338181740375JJ
social0.0188543197850406JJ
environmental0.0221298700466866JJ

Label: 8, with a score of: 0.256781893662136
WordScorePoS
foster0JJ
improvement0NN
performance0NN
resilience0NN
infrastructure0NN
dimension0NN
sustainability0NN
economic0.128463821595706JJ
social0.0504071033101074JJ
environmental0.0295821503613099JJ

Label: 10, with a score of: 0.421213499439985
WordScorePoS
foster0JJ
improvement0NN
performance0.064698038783335NN
resilience0NN
infrastructure0NN
dimension0.0488696285210061NN
sustainability0NN
economic0.103276847978089JJ
social0.101310366625194JJ
environmental0.0237821918091848JJ

Designers infrastructure decision makers and the public currently face proliferation of sustainability rating tools most of which focus on the performance of particular infrastructure element rather than its contribution to the system in which it resides

Label: 9, with a score of: 0.689863044109752
WordScorePoS
infrastructure0.192202909044225NN
decision0NN
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public0.0160169090870187NN
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focus0.030745604615229NN
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Label: 12, with a score of: 0.356496337579097
WordScorePoS
infrastructure0.028867295332595NN
decision0NN
maker0.0409791940864438NN
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tool0.0542519672965169NN
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contribution0NN
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To address this Envision™ instead establishes holistic framework for evaluating and rating infrastructure projects against the needs and values of the community

Label: 9, with a score of: 0.613834621204824
WordScorePoS
establish0VB
holistic0JJ
framework0NN
infrastructure0.192202909044225NN
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community0.0188543197850406NN

Label: 12, with a score of: 0.410533575006852
WordScorePoS
establish0VB
holistic0JJ
framework0.028867295332595NN
infrastructure0.028867295332595NN
project0.0664302389559493NN
value0NN
community0.0169905820958638NN

Label: 11, with a score of: 0.325899609107843
WordScorePoS
establish0VB
holistic0.0731385760866977JJ
framework0.0194583992186006NN
infrastructure0.0194583992186006NN
project0NN
value0NN
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Label: 17, with a score of: 0.325540003254317
WordScorePoS
establish0.048933054487825VB
holistic0JJ
framework0.0130185595639838NN
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value0.036961556179878NN
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It ensures that the sustainability of tomorrow infrastructure is assessed accurately by considering the entire life cycle of projects at systems level

Label: 9, with a score of: 0.425145083611018
WordScorePoS
ensure0.00412562425683033VB
sustainability0NN
tomorrow0NN
infrastructure0.192202909044225NN
life0NN
cycle0NN
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Label: 12, with a score of: 0.611444963399442
WordScorePoS
ensure0.00743561777158133VB
sustainability0.0409791940864438NN
tomorrow0NN
infrastructure0.028867295332595NN
life0.0250071479597636NN
cycle0.108503934593034NN
project0.0664302389559493NN
system0NN
level0.00513687014008325NN

Envision™ not only asks Did we do the project right

Label: 12, with a score of: 0.778049848378303
WordScorePoS
project0.0664302389559493NN

Label: 6, with a score of: 0.489993527444456
WordScorePoS
project0.041835863322702NN

Label: 13, with a score of: 0.393121834169814
WordScorePoS
project0.0335649154577099NN

but also Did we do the right project

Label: 12, with a score of: 0.778049848378303
WordScorePoS
project0.0664302389559493NN

Label: 6, with a score of: 0.489993527444456
WordScorePoS
project0.041835863322702NN

Label: 13, with a score of: 0.393121834169814
WordScorePoS
project0.0335649154577099NN

In addition Envision™ raises the bar on sustainability performance by recognizing efforts that replenish and restore natural resources and ecosystems and by evaluating infrastructure throughout its full life with ratings for design and planning construction operations and decommissioning

Label: 9, with a score of: 0.508710421507175
WordScorePoS
addition0.0368585655748969NN
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natural0JJ
resource0.0185001613201543NN
ecosystem0NN
infrastructure0.192202909044225NN
full0.030745604615229JJ
life0NN
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Label: 15, with a score of: 0.431804618402146
WordScorePoS
addition0.0316735178673487NN
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natural0.016202004209163JJ
resource0.0317953333768269NN
ecosystem0.134075577362426NN
infrastructure0NN
full0JJ
life0.00794883334420673NN
plan0.0137637438743113NN

Label: 12, with a score of: 0.373683024588485
WordScorePoS
addition0NN
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natural0.0509717462875914JJ
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full0JJ
life0.0250071479597636NN
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The initial release of Envision™ addresses the design and planning phase with subsequent phase ratings to follow

Label: 12, with a score of: 0.898191040454769
WordScorePoS
release0.0409791940864438NN
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Within each phase sustainability objectives are organized in three tiers categories subcategories and assessment objectives called credits

Label: 12, with a score of: 0.51672924941307
WordScorePoS
phase0.108503934593034NN
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Sixty credits are arranged into five primary categories that represent primary attributes of sustainable infrastructure solutions Quality of Life Leadership Resource Allocation Natural World and Climate and Risk

Label: 13, with a score of: 0.448367321812162
WordScorePoS
credit0NN
primary0.0145856516935579JJ
represent0VB
sustainable0JJ
infrastructure0NN
solution0.0671298309154199NN
quality0NN
life0.00842350130803634NN
leadership0NN
resource0NN
natural0.0171695137813101JJ
world0.00351929966767313NN
climate0.291881734282273NN
risk0NN

Label: 9, with a score of: 0.401787129973467
WordScorePoS
credit0.0602029956716497NN
primary0.0160169090870187JJ
represent0VB
sustainable0.00901749491165909JJ
infrastructure0.192202909044225NN
solution0.0368585655748969NN
quality0.022550656390892NN
life0NN
leadership0NN
resource0.0185001613201543NN
natural0JJ
world0.00257642711761688NN
climate0NN
risk0NN

Label: 5, with a score of: 0.394866691827535
WordScorePoS
credit0NN
primary0.0970239057629001JJ
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sustainable0.00390173629172995JJ
infrastructure0.024255976440725NN
solution0NN
quality0NN
life0.0140083044329373NN
leadership0.0688661930304451NN
resource0.0280166088658745NN
natural0.0285529457667019JJ
world0.00585260443759492NN
climate0NN
risk0NN

Label: 4, with a score of: 0.339703955854207
WordScorePoS
credit0NN
primary0.108423939529757JJ
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sustainable0.00872035848132902JJ
infrastructure0NN
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quality0.076326555629389NN
life0.0125233923809486NN
leadership0NN
resource0NN
natural0JJ
world0.0017440716962658NN
climate0NN
risk0NN

Each of the credits is explained in detailed guidance manual ANNEX and includes the credit name intent levels of achievement description explanation on how to advance to higher achievement levels evaluation criteria and documentation sources and related credits

Label: 0, with a score of: 1

By meeting objectives within credit projects earn points toward their rating score

Label: 9, with a score of: 0.636895251677759
WordScorePoS
objective0.0368585655748969NN
credit0.0602029956716497NN
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Label: 7, with a score of: 0.491776279977917
WordScorePoS
objective0.0749457204978913NN
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The achievement of points within the credit is scaled to five levels to ensure all efforts to achieve sustainability are rewarded proportionally

Label: 9, with a score of: 0.526030725649398
WordScorePoS
achievement0.0368585655748969NN
credit0.0602029956716497NN
scale0.0368585655748969NN
level0NN
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Label: 1, with a score of: 0.426366146089428
WordScorePoS
achievement0.0736315832425121NN
credit0NN
scale0NN
level0.0113874611101829NN
ensure0.0247250190957801VB
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sustainability0NN

Recognition of the challenges and complexity of achieving sustainability is necessary step in improving infrastructure development

Label: 9, with a score of: 0.589221337667333
WordScorePoS
challenge0NN
achieve0.0114006914853063VB
sustainability0NN
improve0VB
infrastructure0.192202909044225NN
development0.0330049940546426NN

Label: 11, with a score of: 0.377657747674386
WordScorePoS
challenge0.0947742174515357NN
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improve0.0273960270556257VB
infrastructure0.0194583992186006NN
development0.0100241617629471NN

Label: 7, with a score of: 0.350971717719537
WordScorePoS
challenge0.0528748274225028NN
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improve0.0229264916384433VB
infrastructure0.0651354046449052NN
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The purpose of Envision™ is to initiate systemic change that improves not only project performance but the mindsets of designers project owners and decision makers to transform the way infrastructure is designed built and operated

Label: 13, with a score of: 0.629785827582678
WordScorePoS
systemic0JJ
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Label: 7, with a score of: 0.414935098951631
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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Engineering for Sustainable Development The engineering community has myriad important roles to play in improving human living standards and protecting and restoring the environment

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In particular engineers have already taken steps in support of the sustainability goals articulated in the Rio Summit in

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Moving forward the World Federation of Engineering Organizations has identified ways in which the profession can more effectively contribute to sustainable development in the future

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Engineers approach sustainability in terms of the systems they design and build

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Label: 1, with a score of: 0.348990182447369
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A sustainable system is one that is either in equilibrium or one that changes slowly at tolerable rate

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Label: 2, with a score of: 0.489990007234913
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Label: 16, with a score of: 0.342935684700019
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This concept is based on the characteristics of natural ecosystems which consist of nearly closed resource and energy loops that change slowly and are resilient to external shocks

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Label: 13, with a score of: 0.466358000529232
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Label: 15, with a score of: 0.260929661374211
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A closed loop ecosystem model has been proposed to illustrate the roles of engineers in every phase of human ecosystem that mimics natural systems see Figure

Label: 15, with a score of: 0.686326739742138
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Label: 12, with a score of: 0.489222075614284
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Engineers can ' extract and develop natural resources in closedloop low impact systems ' process and modify resources efficiently and with minimal adverse environmental impacts throughout full product life cycles ' design and build transportation infrastructure to improve quality of life and meet human needs ' meet the needs of consumers by designing useful products and services ' recover resources and minimize waste by designing products for reuse and recycling ' produce and distribute non fossil energy and design energy efficient products and ' offset the impacts of industrial activity by designing programs to clean up and reuse old waste sites along with other forms of environmental restoration

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Label: 7, with a score of: 0.441742757705166
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Label: 9, with a score of: 0.340457915904054
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Label: 16, with a score of: 0.0716866907113721
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FIG

Label: 0, with a score of: 1

Illustration of Engineers Definition of Sustainable System Source Reprinted from WFEO ComTech

Label: 2, with a score of: 0.539260599377213
WordScorePoS
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ANNEX As the primary designers of the world infrastructure engineers build projects and deliver services whose ultimate purpose is to meet human needs

Label: 9, with a score of: 0.553203422674654
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Label: 4, with a score of: 0.345331731180186
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Label: 5, with a score of: 0.344433863658103
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Label: 12, with a score of: 0.310041059343735
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Label: 2, with a score of: 0.301818381383365
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Increasingly however addressing the environmental impacts of these projects has become an equally important role

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Label: 10, with a score of: 0.322706867784546
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Given the broad range of issues and sectors in which engineers work they can contribute significantly to achieving sustainability goals along the entire chain of modern production and consumption

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Label: 7, with a score of: 0.534985049438573
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Label: 8, with a score of: 0.319981593847576
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There are approximately million engineers in the world today and they encompass several disciplines civil environmental mechanical electrical chemical industrial agricultural mining petroleum and computer engineers

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approximately0RB
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Label: 2, with a score of: 0.480342707731028
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Engineers are involved with two kinds of projects

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Label: 14, with a score of: 0.306557015749906
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They design and build projects that meet basic human needs potable water food housing sanitation energy transportation communication resource development and industrial processing

Label: 6, with a score of: 0.567489694519712
WordScorePoS
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Label: 9, with a score of: 0.464374616522506
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Label: 12, with a score of: 0.413605072360853
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Label: 7, with a score of: 0.299111385853817
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Label: 11, with a score of: 0.280618792392468
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Label: 2, with a score of: 0.249986495836402
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They solve environmental problems create waste treatment facilities recycle resources clean up and restore polluted sites and protect or restore natural ecosystems

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Label: 15, with a score of: 0.430374593385929
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Label: 7, with a score of: 0.227228568472964
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Label: 6, with a score of: 0.404000612964718
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Label: 14, with a score of: 0.302391370481981
WordScorePoS
environmental0JJ
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Both types can be implemented with sustainable development as primary goal by planning and building projects that preserve natural resources are cost efficient and support human and natural environments

Label: 17, with a score of: 0.38641789489536
WordScorePoS
type0NN
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Label: 12, with a score of: 0.451996721693028
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type0NN
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Label: 11, with a score of: 0.381882132756468
WordScorePoS
type0NN
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Label: 5, with a score of: 0.314132003394191
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type0.0688661930304451NN
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In each phase of the ecosystem model shown in Figure engineers fulfill several functions and can actively contribute to sustainable development

Label: 15, with a score of: 0.41593507940853
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Label: 12, with a score of: 0.620823204862369
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Label: 17, with a score of: 0.330915066551514
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Some examples of these roles ' Developing and extracting natural resources ' Water resource planning of all kinds including dams irrigation systems and wells ' Designing tree plantations and managing forests ' Improved land use planning to protect natural resources from the impact of urban sprawl Processing and Modifying Resources In the past many industries generated waste products that were toxic and not easily degraded under natural conditions

Label: 15, with a score of: 0.461030843963605
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Label: 11, with a score of: 0.421874787343714
WordScorePoS
develop0VB
natural0.0229054731708393JJ
resource0.0449504361447405NN
water0.0458109463416786NN
plan0.0583751976558017NN
kind0NN
include0VB
irrigation0NN
system0.0315914058171786NN
tree0NN
manage0VB
forest0NN
improve0.0273960270556257VB
land0.0389167984372011NN
protect0.0328357290160445VB
impact0.0194583992186006NN
urban0.27622591898666JJ
processing0NN
industry0NN
generate0VB
waste0.0373517915079663NN
product0.0268848280079943NN
degrade0VB
condition0NN

Label: 12, with a score of: 0.415932919996853
WordScorePoS
develop0VB
natural0.0509717462875914JJ
resource0.0416785799329393NN
water0.186896403054502NN
plan0.0144336476662975NN
kind0NN
include0VB
irrigation0NN
system0NN
tree0NN
manage0VB
forest0NN
improve0VB
land0.0144336476662975NN
protect0.0121782716594324VB
impact0.0866018859977851NN
urban0JJ
processing0.0409791940864438NN
industry0NN
generate0VB
waste0.138532104381853NN
product0.0199423462679015NN
degrade0VB
condition0.0409791940864438NN

Label: 6, with a score of: 0.393349520722129
WordScorePoS
develop0VB
natural0.0214003646985842JJ
resource0.0104991907358033NN
water0.556409482163189NN
plan0NN
kind0NN
include0VB
irrigation0.0516150472983178NN
system0NN
tree0NN
manage0VB
forest0.041835863322702NN
improve0.0511916925627245VB
land0NN
protect0.0153390539205676VB
impact0.0181797964452808NN
urban0JJ
processing0NN
industry0NN
generate0VB
waste0NN
product0NN
degrade0VB
condition0NN

Label: 9, with a score of: 0.229387253131784
WordScorePoS
develop0VB
natural0JJ
resource0.0185001613201543NN
water0.0377086395700813NN
plan0NN
kind0NN
include0VB
irrigation0.0454743001434393NN
system0NN
tree0NN
manage0VB
forest0NN
improve0VB
land0NN
protect0VB
impact0.0160169090870187NN
urban0JJ
processing0.0909486002868787NN
industry0.181897200573757NN
generate0VB
waste0NN
product0.0442597400933733NN
degrade0VB
condition0NN

In the last years this has led to environmental pollution and new laws and regulations to help protect the environment

Label: 16, with a score of: 0.435549570743972
WordScorePoS
lead0NN
environmental0JJ
pollution0NN
law0.164686343640793NN
regulation0NN
protect0.0241291244414115VB
environment0NN

Label: 12, with a score of: 0.443354108437848
WordScorePoS
lead0.0169905820958638NN
environmental0.0598270388037045JJ
pollution0.0234335422829798NN
law0NN
regulation0NN
protect0.0121782716594324VB
environment0.079769385071606NN

Label: 8, with a score of: 0.36266358861199
WordScorePoS
lead0.0504071033101074NN
environmental0.0295821503613099JJ
pollution0NN
law0NN
regulation0NN
protect0.0180650490434051VB
environment0.0591643007226199NN

Label: 10, with a score of: 0.356537014665771
WordScorePoS
lead0NN
environmental0.0237821918091848JJ
pollution0NN
law0.0330412182586772NN
regulation0.0977392570420122NN
protect0VB
environment0NN

Label: 11, with a score of: 0.334666632230499
WordScorePoS
lead0NN
environmental0.0537696560159885JJ
pollution0.0315914058171786NN
law0NN
regulation0NN
protect0.0328357290160445VB
environment0.0268848280079943NN

Label: 14, with a score of: 0.311903777465608
WordScorePoS
lead0NN
environmental0JJ
pollution0.0689352823740101NN
law0.0543366405761884NN
regulation0NN
protect0.0119417512090511VB
environment0NN

Many industries are now making major changes in the ways they use raw materials to produce products

Label: 9, with a score of: 0.787959248174821
WordScorePoS
industry0.181897200573757NN
major0.0160169090870187JJ
material0NN
produce0VB
product0.0442597400933733NN

Label: 14, with a score of: 0.342689893901544
WordScorePoS
industry0.0401833161726777NN
major0JJ
material0NN
produce0.0651400632663124VB
product0NN

By reducing their waste to minimum many are finding that resource efficient processing leads to increased profits

Label: 12, with a score of: 0.673481898079678
WordScorePoS
reduce0.022306853314744VB
waste0.138532104381853NN
resource0.0416785799329393NN
processing0.0409791940864438NN
lead0.0169905820958638NN
increase0.00958677785775002NN

Label: 9, with a score of: 0.359741770003143
WordScorePoS
reduce0VB
waste0NN
resource0.0185001613201543NN
processing0.0909486002868787NN
lead0.0188543197850406NN
increase0.0159575617614215NN

Label: 7, with a score of: 0.312872253432606
WordScorePoS
reduce0.00838876601975825VB
waste0.0625160381065121NN
resource0NN
processing0NN
lead0.0383371018038178NN
increase0.0162235147372888NN

Engineers play the following roles in processing and modifying resources ' They change industrial processes to reduce the use of energy resources and waste

Label: 7, with a score of: 0.577706220072357
WordScorePoS
processing0NN
resource0NN
change0.105749654845006NN
industrial0JJ
process0NN
reduce0.00838876601975825VB
energy0.494617163780726NN
waste0.0625160381065121NN

Label: 9, with a score of: 0.494029578191855
WordScorePoS
processing0.0909486002868787NN
resource0.0185001613201543NN
change0NN
industrial0.318320101004075JJ
process0.0737171311497939NN
reduce0VB
energy0.0540565419125769NN
waste0NN

Label: 13, with a score of: 0.395916999315731
WordScorePoS
processing0NN
resource0NN
change0.402565546215053NN
industrial0.0414107553022249JJ
process0NN
reduce0.00375696197697663VB
energy0.0123065238088614NN
waste0NN

Label: 12, with a score of: 0.360714857153056
WordScorePoS
processing0.0409791940864438NN
resource0.0416785799329393NN
change0NN
industrial0JJ
process0NN
reduce0.022306853314744VB
energy0.133960988253756NN
waste0.138532104381853NN

' They consider the total input and output of operations over their complete life cycles

Label: 12, with a score of: 0.757598232776485
WordScorePoS
total0.0468670845659596NN
input0NN
complete0JJ
life0.0250071479597636NN
cycle0.108503934593034NN

Label: 4, with a score of: 0.394932558368994
WordScorePoS
total0NN
input0NN
complete0.0815069365350084JJ
life0.0125233923809486NN
cycle0NN

Label: 2, with a score of: 0.350879808742243
WordScorePoS
total0.0252000451111224NN
input0.0583416713841148NN
complete0JJ
life0NN
cycle0NN

' They apply the principles of industrial ecology for example creating eco industrial parks where several industries are co located and waste products are used as the raw materials for other industries

Label: 9, with a score of: 0.957593837837149
WordScorePoS
principle0NN
industrial0.318320101004075JJ
create0.0520080474473126VB
industry0.181897200573757NN
waste0NN
product0.0442597400933733NN
material0NN

Label: 12, with a score of: 0.158834315091723
WordScorePoS
principle0NN
industrial0JJ
create0.0234335422829798VB
industry0NN
waste0.138532104381853NN
product0.0199423462679015NN
material0NN

Transportation Engineers are at the forefront of developing transportation systems including designing and building all weather roads and highways designing more efficient engines and transportation vehicles and constructing railroads and high speed rail systems

Label: 12, with a score of: 0.547702801849394
WordScorePoS
transportation0.0332151194779746NN
develop0VB
system0NN
include0VB
building0NN
weather0NN
road0NN
vehicle0.217007869186068NN

Label: 17, with a score of: 0.332462572162979
WordScorePoS
transportation0NN
develop0VB
system0.0211360962287062NN
include0VB
building0.149940347231585NN
weather0NN
road0NN
vehicle0NN

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS In the future engineers will design these transportation systems that ' Improve methods to collect and reuse construction materials from the built environment

Label: 11, with a score of: 0.706565692673374
WordScorePoS
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
future0.0373517915079663NN
transportation0.04477822029736NN
system0.0315914058171786NN
improve0.0273960270556257VB
reuse0NN
material0.055245183797332NN
build0VB
environment0.0268848280079943NN

Label: 2, with a score of: 0.311024398393019
WordScorePoS
urban0JJ
development0.0159922815774215NN
knowledge0.0595900566394012NN
future0.0297950283197006NN
transportation0NN
system0.0756001353333673NN
improve0.0218534471578307VB
reuse0NN
material0NN
build0VB
environment0.0214456704626236NN

' are more energy efficient and create fewer adverse environmental impacts ' Recover reuse and remanufacture by products from industrial processing

Label: 9, with a score of: 0.613812204339276
WordScorePoS
energy0.0540565419125769NN
create0.0520080474473126VB
adverse0JJ
environmental0.0221298700466866JJ
impact0.0160169090870187NN
reuse0NN
product0.0442597400933733NN
industrial0.318320101004075JJ
processing0.0909486002868787NN

Label: 7, with a score of: 0.507916733312933
WordScorePoS
energy0.494617163780726NN
create0VB
adverse0JJ
environmental0JJ
impact0NN
reuse0NN
product0NN
industrial0JJ
processing0NN

Label: 12, with a score of: 0.484850006476144
WordScorePoS
energy0.133960988253756NN
create0.0234335422829798VB
adverse0.0664302389559493JJ
environmental0.0598270388037045JJ
impact0.0866018859977851NN
reuse0.0409791940864438NN
product0.0199423462679015NN
industrial0JJ
processing0.0409791940864438NN

' encourage sound urban and rural planning with less urban sprawl and Environmental Restoration ' have longer lived facilities maintained at lower costs

Label: 11, with a score of: 0.832747395857312
WordScorePoS
encourage0VB
sound0NN
urban0.27622591898666JJ
rural0.0373517915079663JJ
plan0.0583751976558017NN
environmental0.0537696560159885JJ
restoration0NN
live0.0269743071558856VB
facility0NN
maintain0.055245183797332VB
cost0NN

Label: 2, with a score of: 0.26703434094979
WordScorePoS
encourage0VB
sound0NN
urban0JJ
rural0.148975141598503JJ
plan0NN
environmental0JJ
restoration0NN
live0.0143446978608332VB
facility0NN
maintain0.0881366997038154VB
cost0NN

Label: 10, with a score of: 0.317442308305225
WordScorePoS
encourage0.02794560829783VB
sound0NN
urban0.0488696285210061JJ
rural0.0330412182586772JJ
plan0.0344256159926965NN
environmental0.0237821918091848JJ
restoration0NN
live0.0159075630937118VB
facility0NN
maintain0VB
cost0.0660824365173543NN

that can be ' Meeting Consumer Needs As the rapid urbanization of the developing world continues in the coming decades the engineering profession will have growing responsibility to help provide shelter infrastructure and other resources to this population

Label: 12, with a score of: 0.584913061123075
WordScorePoS
consumer0.325511803779102NN
rapid0JJ
urbanization0NN
develop0VB
world0.00464349782489754NN
continue0.0199423462679015VB
decade0NN
engineering0NN
grow0.0339811641917276VB
responsibility0NN
provide0.0200087191734851VB
shelter0NN
infrastructure0.028867295332595NN
resource0.0416785799329393NN
population0.0144336476662975NN

Label: 11, with a score of: 0.517440536928963
WordScorePoS
consumer0NN
rapid0.0373517915079663JJ
urbanization0.146277152173395NN
develop0VB
world0.00626002771693937NN
continue0.0537696560159885VB
decade0.055245183797332NN
engineering0NN
grow0.0229054731708393VB
responsibility0NN
provide0.0269743071558856VB
shelter0NN
infrastructure0.0194583992186006NN
resource0.0449504361447405NN
population0.0194583992186006NN

Label: 9, with a score of: 0.323249439223761
WordScorePoS
consumer0NN
rapid0.030745604615229JJ
urbanization0NN
develop0VB
world0.00257642711761688NN
continue0VB
decade0NN
engineering0NN
grow0.0188543197850406VB
responsibility0NN
provide0.00740117451847634VB
shelter0NN
infrastructure0.192202909044225NN
resource0.0185001613201543NN
population0NN

The roles of engineers in meeting human needs include ' Creative land planning and development to minimize negative environmental impacts ' Designing housing and commercial buildings streets utility lines public transportation and other infrastructure ' Reducing the risks of damage and loss of life from natural hazards Resource Recovery and Reuse For sustainable development to be possible human activities will have to be redesigned to maximize resource efficiency

Label: 11, with a score of: 0.560378283998575
WordScorePoS
human0.0273960270556257JJ
include0VB
land0.0389167984372011NN
plan0.0583751976558017NN
development0.0100241617629471NN
minimize0VB
environmental0.0537696560159885JJ
impact0.0194583992186006NN
housing0.219415728260093NN
commercial0JJ
building0.0747035830159326NN
line0.0373517915079663NN
public0.0583751976558017NN
transportation0.04477822029736NN
infrastructure0.0194583992186006NN
reduce0.0200483235258943VB
risk0.0631828116343571NN
loss0.0315914058171786NN
life0.0112376090361851NN
natural0.0229054731708393JJ
hazard0NN
resource0.0449504361447405NN
recovery0NN
reuse0NN
sustainable0.00782503464617421JJ
activity0NN

Label: 9, with a score of: 0.243836806890119
WordScorePoS
human0.011275328195446JJ
include0VB
land0NN
plan0NN
development0.0330049940546426NN
minimize0VB
environmental0.0221298700466866JJ
impact0.0160169090870187NN
housing0NN
commercial0JJ
building0NN
line0.030745604615229NN
public0.0160169090870187NN
transportation0NN
infrastructure0.192202909044225NN
reduce0VB
risk0NN
loss0NN
life0NN
natural0JJ
hazard0NN
resource0.0185001613201543NN
recovery0NN
reuse0NN
sustainable0.00901749491165909JJ
activity0NN

Label: 17, with a score of: 0.236309461520773
WordScorePoS
human0JJ
include0VB
land0NN
plan0.0130185595639838NN
development0.0469463607292177NN
minimize0VB
environmental0JJ
impact0NN
housing0NN
commercial0JJ
building0.149940347231585NN
line0NN
public0.0390556786919513NN
transportation0NN
infrastructure0.0130185595639838NN
reduce0.00335331148065841VB
risk0NN
loss0NN
life0NN
natural0JJ
hazard0NN
resource0.0375923735942499NN
recovery0NN
reuse0NN
sustainable0.0115176738164035JJ
activity0NN

Label: 12, with a score of: 0.448681244877482
WordScorePoS
human0.0101607690729067JJ
include0VB
land0.0144336476662975NN
plan0.0144336476662975NN
development0.022306853314744NN
minimize0.0664302389559493VB
environmental0.0598270388037045JJ
impact0.0866018859977851NN
housing0NN
commercial0.0542519672965169JJ
building0NN
line0NN
public0.028867295332595NN
transportation0.0332151194779746NN
infrastructure0.028867295332595NN
reduce0.022306853314744VB
risk0NN
loss0.0468670845659596NN
life0.0250071479597636NN
natural0.0509717462875914JJ
hazard0NN
resource0.0416785799329393NN
recovery0NN
reuse0.0409791940864438NN
sustainable0.0162522423871414JJ
activity0.0199423462679015NN

Engineers can assist in this process in several ways ' Design better solid waste collection and storage facilities

Label: 12, with a score of: 0.612490885777409
WordScorePoS
assist0VB
process0NN
waste0.138532104381853NN
collection0NN
facility0NN

Some environmental pollution is inevitable in the future resulting from resource extraction industrial processing and transportation and wastes generated by humans

Label: 9, with a score of: 0.602077896586163
WordScorePoS
environmental0.0221298700466866JJ
pollution0NN
inevitable0JJ
future0NN
result0NN
resource0.0185001613201543NN
industrial0.318320101004075JJ
processing0.0909486002868787NN
transportation0NN
waste0NN
generate0VB
human0.011275328195446JJ

Label: 12, with a score of: 0.490270374680887
WordScorePoS
environmental0.0598270388037045JJ
pollution0.0234335422829798NN
inevitable0JJ
future0.0277064208763707NN
result0NN
resource0.0416785799329393NN
industrial0JJ
processing0.0409791940864438NN
transportation0.0332151194779746NN
waste0.138532104381853NN
generate0VB
human0.0101607690729067JJ

Label: 11, with a score of: 0.36187978018195
WordScorePoS
environmental0.0537696560159885JJ
pollution0.0315914058171786NN
inevitable0JJ
future0.0373517915079663NN
result0NN
resource0.0449504361447405NN
industrial0JJ
processing0NN
transportation0.04477822029736NN
waste0.0373517915079663NN
generate0VB
human0.0273960270556257JJ

Label: 14, with a score of: 0.356630979264623
WordScorePoS
environmental0JJ
pollution0.0689352823740101NN
inevitable0JJ
future0.0543366405761884NN
result0NN
resource0.0572167664438909NN
industrial0JJ
processing0NN
transportation0.0325700316331562NN
waste0NN
generate0.0401833161726777VB
human0.0199268631468397JJ

The impacts of residual wastes should be offset by variety of environmental restoration projects such as ' Treating and restoring old industrial waste sites ' Restoring the ecology of lakes and wetlands ' Renewing aging urban areas in large cities Energy Production and Use The long term effects of increased fossil energy use with attendant greenhouse gas emissions will produce major changes in the Earth climate

Label: 7, with a score of: 0.628466403049717
WordScorePoS
impact0NN
waste0.0625160381065121NN
variety0NN
environmental0JJ
restoration0NN
project0NN
restore0VB
industrial0JJ
lake0NN
wetland0NN
age0NN
urban0JJ
city0NN
energy0.494617163780726NN
production0.0383371018038178NN
term0.0749457204978913NN
increase0.0162235147372888NN
fossil0.0924643738905717NN
greenhouse0.0749457204978913NN
gas0.0749457204978913NN
emission0.0625160381065121NN
produce0VB
major0.0325677023224526JJ
earth0NN
climate0.115011305411453NN

Label: 11, with a score of: 0.458923146910519
WordScorePoS
impact0.0194583992186006NN
waste0.0373517915079663NN
variety0NN
environmental0.0537696560159885JJ
restoration0NN
project0NN
restore0VB
industrial0JJ
lake0NN
wetland0NN
age0NN
urban0.27622591898666JJ
city0.607697021770652NN
energy0.0492535935240667NN
production0NN
term0NN
increase0.00323105002784081NN
fossil0NN
greenhouse0NN
gas0NN
emission0.0373517915079663NN
produce0VB
major0JJ
earth0.04477822029736NN
climate0.0229054731708393NN

Label: 12, with a score of: 0.416223627937974
WordScorePoS
impact0.0866018859977851NN
waste0.138532104381853NN
variety0NN
environmental0.0598270388037045JJ
restoration0NN
project0.0664302389559493NN
restore0VB
industrial0JJ
lake0.0409791940864438NN
wetland0NN
age0NN
urban0JJ
city0NN
energy0.133960988253756NN
production0.118934074671047NN
term0NN
increase0.00958677785775002NN
fossil0.0409791940864438NN
greenhouse0.0332151194779746NN
gas0.0332151194779746NN
emission0.0554128417527413NN
produce0.0332151194779746VB
major0JJ
earth0NN
climate0NN

Label: 13, with a score of: 0.299807650778938
WordScorePoS
impact0.0437569550806737NN
waste0NN
variety0NN
environmental0JJ
restoration0NN
project0.0335649154577099NN
restore0VB
industrial0.0414107553022249JJ
lake0NN
wetland0NN
age0NN
urban0JJ
city0NN
energy0.0123065238088614NN
production0NN
term0NN
increase0.0145316075945746NN
fossil0NN
greenhouse0.0671298309154199NN
gas0.0671298309154199NN
emission0.19598742448524NN
produce0VB
major0.0291713033871158JJ
earth0NN
climate0.291881734282273NN

Label: 9, with a score of: 0.190963671185457
WordScorePoS
impact0.0160169090870187NN
waste0NN
variety0NN
environmental0.0221298700466866JJ
restoration0NN
project0NN
restore0VB
industrial0.318320101004075JJ
lake0NN
wetland0NN
age0NN
urban0JJ
city0NN
energy0.0540565419125769NN
production0.0188543197850406NN
term0NN
increase0.0159575617614215NN
fossil0NN
greenhouse0NN
gas0NN
emission0NN
produce0VB
major0.0160169090870187JJ
earth0NN
climate0NN

Label: 17, with a score of: 0.0709192529022274
WordScorePoS
impact0NN
waste0NN
variety0NN
environmental0JJ
restoration0NN
project0NN
restore0VB
industrial0JJ
lake0NN
wetland0NN
age0.0153248097886055NN
urban0JJ
city0NN
energy0.0109843026967392NN
production0NN
term0.149793392961411NN
increase0.00432344066632653NN
fossil0NN
greenhouse0NN
gas0NN
emission0NN
produce0VB
major0JJ
earth0NN
climate0NN

One of the greatest engineering challenges will be to develop less carbon intensive energy sources while simultaneously reducing total energy consumption

Label: 7, with a score of: 0.815976217172185
WordScorePoS
engineering0NN
challenge0.0528748274225028NN
develop0VB
carbon0.0625160381065121NN
intensive0JJ
energy0.494617163780726NN
source0.0274787313211514NN
reduce0.00838876601975825VB
total0.0528748274225028NN
consumption0NN

Label: 12, with a score of: 0.457603978927102
WordScorePoS
engineering0NN
challenge0NN
develop0VB
carbon0NN
intensive0JJ
energy0.133960988253756NN
source0NN
reduce0.022306853314744VB
total0.0468670845659596NN
consumption0.332151194779746NN

Label: 8, with a score of: 0.18363359260429
WordScorePoS
engineering0NN
challenge0.0347609334428716NN
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carbon0NN
intensive0.0804764811724088JJ
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consumption0.147812295323721NN

The roles of engineers in energy production may include the following ' More efficiently extracting and processing remaining petroleum and gas reserves ' Improving the efficiency of electric power generation transmission and distribution ' Expanding the use of renewables hydroelectric solar geothermal wind and biomass energy

Label: 7, with a score of: 0.859477875132032
WordScorePoS
energy0.494617163780726NN
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gas0.0749457204978913NN
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power0NN
generation0NN
expand0.0449973847138318VB
renewable0.158624482267508JJ

Label: 12, with a score of: 0.366305962463897
WordScorePoS
energy0.133960988253756NN
production0.118934074671047NN
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gas0.0332151194779746NN
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power0NN
generation0.0819583881728876NN
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renewable0.0234335422829798JJ

ANNEX Engineers can also play role in conserving and reducing the use of energy in the following ways ' Designing energy efficient buildings and industrial processes ' Designing more efficient automobiles and public transportation systems The engineering profession will be called upon to fulfill these and many other roles in the design building operation and maintenance of sustainable cities

Label: 11, with a score of: 0.607342654708383
WordScorePoS
conserve0VB
reduce0.0200483235258943VB
energy0.0492535935240667NN
building0.0747035830159326NN
industrial0JJ
process0NN
public0.0583751976558017NN
transportation0.04477822029736NN
system0.0315914058171786NN
engineering0NN
sustainable0.00782503464617421JJ
city0.607697021770652NN

Label: 7, with a score of: 0.601301019085191
WordScorePoS
conserve0VB
reduce0.00838876601975825VB
energy0.494617163780726NN
building0NN
industrial0JJ
process0NN
public0NN
transportation0NN
system0NN
engineering0NN
sustainable0.0104774661535722JJ
city0NN

Label: 9, with a score of: 0.313256553903356
WordScorePoS
conserve0VB
reduce0VB
energy0.0540565419125769NN
building0NN
industrial0.318320101004075JJ
process0.0737171311497939NN
public0.0160169090870187NN
transportation0NN
system0NN
engineering0NN
sustainable0.00901749491165909JJ
city0NN

Label: 17, with a score of: 0.236745852291471
WordScorePoS
conserve0VB
reduce0.00335331148065841VB
energy0.0109843026967392NN
building0.149940347231585NN
industrial0JJ
process0NN
public0.0390556786919513NN
transportation0NN
system0.0211360962287062NN
engineering0NN
sustainable0.0115176738164035JJ
city0NN

Label: 12, with a score of: 0.219836799218723
WordScorePoS
conserve0VB
reduce0.022306853314744VB
energy0.133960988253756NN
building0NN
industrial0JJ
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public0.028867295332595NN
transportation0.0332151194779746NN
system0NN
engineering0NN
sustainable0.0162522423871414JJ
city0NN

Accomplishments of the Profession Since the First UN Conference on Environment and Development Following the Rio Summit in group of engineers made systematic analysis of the conference primary action document Agenda

Label: 5, with a score of: 0.883724032733796
WordScorePoS
conference0.18234260265061NN
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They found that of the issues in Agenda have engineering or technical implications and at least appeared to have major engineering implications

Label: 4, with a score of: 0.850026122343489
WordScorePoS
issue0NN
agenda0NN
engineering0.0815069365350084NN
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major0.0216847879059514JJ

Label: 16, with a score of: 0.301461930523105
WordScorePoS
issue0NN
agenda0.0811931365363091NN
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major0.0285977592242194JJ

In the years since the Rio Summit of progress has been slow but encouraging

Label: 8, with a score of: 0.843341698852676
WordScorePoS
progress0.0591643007226199NN
slow0.0607878664258118JJ
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Label: 3, with a score of: 0.336942537246388
WordScorePoS
progress0.0449274832628543NN
slow0.0307735781890304JJ
encourage0VB

The accomplishments include the following ' International engineering organizations formed new entity the World Engineering Partnership for Sustainable Development WEPSD

Label: 17, with a score of: 0.788099851121906
WordScorePoS
include0VB
international0.00648516099948979JJ
engineering0NN
organization0.0422721924574123NN
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world0.00418824502414673NN
partnership0.3914644359026NN
sustainable0.0115176738164035JJ
development0.0469463607292177NN

Label: 16, with a score of: 0.349601054726784
WordScorePoS
include0VB
international0.0189945307832458JJ
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organization0NN
form0.158049144643351NN
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development0.0368309542451156NN

Engineering societies also formed environmental committees at both national and global levels to consider environmental issues

Label: 16, with a score of: 0.626334305838851
WordScorePoS
engineering0NN
society0.118536858482514NN
form0.158049144643351NN
environmental0JJ
national0.0294647633960925JJ
global0.00736619084902312JJ
level0.0610668815653026NN
issue0NN

Label: 8, with a score of: 0.355725631642161
WordScorePoS
engineering0NN
society0.0591643007226199NN
form0.0591643007226199NN
environmental0.0295821503613099JJ
national0.0055149369084567JJ
global0.0220597476338268JJ
level0.00761994916892276NN
issue0NN

Label: 1, with a score of: 0.34237348108325
WordScorePoS
engineering0NN
society0NN
form0.0884167543187491NN
environmental0.0442083771593746JJ
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global0JJ
level0.0113874611101829NN
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' Many engineering organizations developed environmental policies codes of ethics and sustainable development guidelines

Label: 17, with a score of: 0.447644559789193
WordScorePoS
engineering0NN
organization0.0422721924574123NN
develop0VB
environmental0JJ
policy0.0641521854453136NN
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development0.0469463607292177NN
guideline0NN

Label: 10, with a score of: 0.422610603440794
WordScorePoS
engineering0NN
organization0.02794560829783NN
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environmental0.0237821918091848JJ
policy0.0848203862485078NN
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guideline0NN

Label: 8, with a score of: 0.331109396809667
WordScorePoS
engineering0NN
organization0.0347609334428716NN
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environmental0.0295821503613099JJ
policy0.0452169560888236NN
sustainable0.00688808874408283JJ
development0.0055149369084567NN
guideline0NN

Label: 12, with a score of: 0.322271547640034
WordScorePoS
engineering0NN
organization0NN
develop0VB
environmental0.0598270388037045JJ
policy0.0203215381458133NN
sustainable0.0162522423871414JJ
development0.022306853314744NN
guideline0NN

Label: 4, with a score of: 0.32077128998157
WordScorePoS
engineering0.0815069365350084NN
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Label: 1, with a score of: 0.301041274508325
WordScorePoS
engineering0NN
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' Engineering groups contributed to the creation of the Earth Charter

Label: 8, with a score of: 0.598762776254279
WordScorePoS
engineering0NN
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creation0.160952962344818NN
earth0NN

Label: 14, with a score of: 0.666699778446943
WordScorePoS
engineering0NN
contribute0.0815049608642826VB
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earth0.0977100948994686NN

Label: 4, with a score of: 0.30321479575585
WordScorePoS
engineering0.0815069365350084NN
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creation0NN
earth0NN

' Educational programs were started to introduce sustainable development concepts to engineering students and practicing engineers

Label: 0, with a score of: 1

' Industrial processes were improved to reduce the use of resources in manufacturing and to reduce waste products

Label: 9, with a score of: 0.853000628146825
WordScorePoS
industrial0.318320101004075JJ
process0.0737171311497939NN
improve0VB
reduce0VB
resource0.0185001613201543NN
manufacture0.240811982686599NN
waste0NN
product0.0442597400933733NN

Label: 12, with a score of: 0.300148712420272
WordScorePoS
industrial0JJ
process0NN
improve0VB
reduce0.022306853314744VB
resource0.0416785799329393NN
manufacture0NN
waste0.138532104381853NN
product0.0199423462679015NN

Future Goals Engineers believe that many of the challenges of sustainable urban development can be solved by using existing knowledge technology and experience combined with new innovations

Label: 11, with a score of: 0.679129346429579
WordScorePoS
future0.0373517915079663NN
goal0NN
challenge0.0947742174515357NN
sustainable0.00782503464617421JJ
urban0.27622591898666JJ
development0.0100241617629471NN
exist0.04477822029736VB
knowledge0NN
technology0NN
experience0NN
innovation0.0373517915079663NN

Label: 17, with a score of: 0.478478746428756
WordScorePoS
future0NN
goal0NN
challenge0NN
sustainable0.0115176738164035JJ
urban0JJ
development0.0469463607292177NN
exist0.0599173571845643VB
knowledge0.0499801157438617NN
technology0.109843026967392NN
experience0.0299586785922821NN
innovation0.0499801157438617NN

Label: 9, with a score of: 0.328775357279929
WordScorePoS
future0NN
goal0NN
challenge0NN
sustainable0.00901749491165909JJ
urban0JJ
development0.0330049940546426NN
exist0VB
knowledge0NN
technology0.0810848128688654NN
experience0NN
innovation0.122982418460916NN

For the engineering profession these innovations will be in the following areas ' Sharing information

Label: 9, with a score of: 0.701727190424179
WordScorePoS
engineering0NN
innovation0.122982418460916NN
share0.0377086395700813NN
information0.0675706773907211NN

Label: 17, with a score of: 0.456745640391351
WordScorePoS
engineering0NN
innovation0.0499801157438617NN
share0.0766240489430275NN
information0.0219686053934784NN

Label: 8, with a score of: 0.330177732904521
WordScorePoS
engineering0NN
innovation0.0821985033584295NN
share0.0252035516550537NN
information0NN

Label: 4, with a score of: 0.306817469510461
WordScorePoS
engineering0.0815069365350084NN
innovation0NN
share0NN
information0.0182963616752599NN

Creating comprehensive program to identify and provide the information that engineers in developing countries need to meet energy requirements as well as other basic human needs like food health services and infrastructure

Label: 7, with a score of: 0.546869581907873
WordScorePoS
create0VB
identify0VB
provide0VB
information0NN
develop0VB
country0NN
meet0VB
energy0.494617163780726NN
basic0JJ
human0JJ
food0.0383371018038178NN
health0NN
service0.0451471467893167NN
infrastructure0.0651354046449052NN

Label: 9, with a score of: 0.42843377691207
WordScorePoS
create0.0520080474473126VB
identify0VB
provide0.00740117451847634VB
information0.0675706773907211NN
develop0VB
country0NN
meet0VB
energy0.0540565419125769NN
basic0.0480507272610562JJ
human0.011275328195446JJ
food0.0188543197850406NN
health0.0377086395700813NN
service0.0148023490369527NN
infrastructure0.192202909044225NN

Label: 12, with a score of: 0.420795897312092
WordScorePoS
create0.0234335422829798VB
identify0VB
provide0.0200087191734851VB
information0.0365348149782971NN
develop0VB
country0NN
meet0VB
energy0.133960988253756NN
basic0.0144336476662975JJ
human0.0101607690729067JJ
food0.186896403054502NN
health0.0339811641917276NN
service0.00666957305782837NN
infrastructure0.028867295332595NN

Label: 2, with a score of: 0.389431023713507
WordScorePoS
create0VB
identify0VB
provide0.0215170467912497VB
information0.0130963126055467NN
develop0VB
country0NN
meet0.0881366997038154VB
energy0.0130963126055467NN
basic0JJ
human0JJ
food0.292342270824999NN
health0NN
service0.0143446978608332NN
infrastructure0.0155217067874935NN

Label: 3, with a score of: 0.213685975072092
WordScorePoS
create0VB
identify0VB
provide0.0100171139310145VB
information0.00914534810831654NN
develop0VB
country0NN
meet0.0307735781890304VB
energy0NN
basic0JJ
human0JJ
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health0.191387734041787NN
service0.0100171139310145NN
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' Global education programs

Label: 4, with a score of: 0.914251324032038
WordScorePoS
global0.00558555252470789JJ
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Expanding global educational programs on sustainable development for students and practicing engineers in order to make sustainable development more understandable and easier to apply to real world engineering projects

Label: 12, with a score of: 0.519737886379627
WordScorePoS
expand0VB
global0.022306853314744JJ
educational0JJ
sustainable0.0162522423871414JJ
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' Engineers as environmental generalists

Label: 12, with a score of: 0.587918376437846
WordScorePoS
environmental0.0598270388037045JJ

Label: 11, with a score of: 0.528392671585544
WordScorePoS
environmental0.0537696560159885JJ

Label: 1, with a score of: 0.434434293326282
WordScorePoS
environmental0.0442083771593746JJ

Encouraging more engineers to become environmental generalists to broaden perspectives in engineering and equip them to assume leadership roles in education industry and government

Label: 4, with a score of: 0.64348015701239
WordScorePoS
encourage0VB
environmental0JJ
broaden0VB
perspective0NN
engineering0.0815069365350084NN
leadership0NN
education0.244244978014045NN
industry0NN
government0NN

Label: 9, with a score of: 0.498942100912633
WordScorePoS
encourage0.0260040237236563VB
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leadership0NN
education0.022550656390892NN
industry0.181897200573757NN
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' Engineers in decision making

Label: 1, with a score of: 0.594600345793648
WordScorePoS
decision0.0519476926891779NN

Label: 16, with a score of: 0.531438169051852
WordScorePoS
decision0.0464294830712827NN

Label: 5, with a score of: 0.450753907225948
WordScorePoS
decision0.0393804437159645NN

Becoming actively engaged in the full range of decision making processes in addition to designing and imple URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS menting projects

Label: 11, with a score of: 0.55063770899126
WordScorePoS
engage0VB
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urban0.27622591898666JJ
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Label: 9, with a score of: 0.493715886810345
WordScorePoS
engage0.0454743001434393VB
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Label: 12, with a score of: 0.328354032391813
WordScorePoS
engage0.0819583881728876VB
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urban0JJ
development0.022306853314744NN
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Engineers can help direct the course of important projects and foster sustainable development by involving themselves in all stages of project decision making

Label: 12, with a score of: 0.631285378094066
WordScorePoS
direct0JJ
project0.0664302389559493NN
foster0JJ
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involve0.0819583881728876VB
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Label: 9, with a score of: 0.367990624846195
WordScorePoS
direct0JJ
project0NN
foster0.0454743001434393JJ
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Label: 17, with a score of: 0.365052128883929
WordScorePoS
direct0.0299586785922821JJ
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decision0.0211360962287062NN

Label: 10, with a score of: 0.337706980587155
WordScorePoS
direct0.0396106020715138JJ
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' Environmental impacts and costs

Label: 12, with a score of: 0.696605515245961
WordScorePoS
environmental0.0598270388037045JJ
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Label: 10, with a score of: 0.359491609667817
WordScorePoS
environmental0.0237821918091848JJ
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cost0.0660824365173543NN

Label: 16, with a score of: 0.334003578240528
WordScorePoS
environmental0JJ
impact0.0285977592242194NN
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Improving methods for identifying and considering all of project environmental costs and impacts throughout project life cycle

Label: 12, with a score of: 0.804960044045752
WordScorePoS
improve0VB
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project0.0664302389559493NN
environmental0.0598270388037045JJ
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impact0.0866018859977851NN
life0.0250071479597636NN
cycle0.108503934593034NN

Practical approaches should be developed that would alter conventional accounting practices to factor in the direct and indirect environmental costs of facility through its life cycle of operations

Label: 12, with a score of: 0.77640120713703
WordScorePoS
approach0.0542519672965169NN
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' Direct assistance programs

Label: 11, with a score of: 0.57925742476808
WordScorePoS
direct0.04477822029736JJ
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Label: 17, with a score of: 0.56380291599102
WordScorePoS
direct0.0299586785922821JJ
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Label: 10, with a score of: 0.51240838061648
WordScorePoS
direct0.0396106020715138JJ
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Creating programs to provide hands on help share knowledge and provide assistance on technically viable commercially feasible and socially sustainable projects in developing countries

Label: 17, with a score of: 0.465928522450006
WordScorePoS
create0VB
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share0.0766240489430275NN
knowledge0.0499801157438617NN
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feasible0JJ
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project0NN
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country0NN

For example engineering firms that have extensive experience should be encouraged to partner with engineers in less developed countries

Label: 0, with a score of: 1

This teaming built into project requirements could be an effective way to increase the capabilities of local engineering firms and practitioners

Label: 9, with a score of: 0.499390982297437
WordScorePoS
build0.030745604615229VB
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Label: 4, with a score of: 0.498968813172547
WordScorePoS
build0.0416255041156371VB
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Label: 12, with a score of: 0.476818334872108
WordScorePoS
build0VB
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' Policy principles and partnerships

Label: 17, with a score of: 0.926855597798596
WordScorePoS
policy0.0641521854453136NN
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Label: 10, with a score of: 0.343511822154132
WordScorePoS
policy0.0848203862485078NN
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Supporting well crafted policies and creative applications of engineering principles and committing to partnerships with social and physical scientists and health and medical professionals

Label: 17, with a score of: 0.731811741485209
WordScorePoS
support0.0451108483130998NN
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scientist0NN
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Label: 3, with a score of: 0.269004476301596
WordScorePoS
support0.00625975725959118NN
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principle0NN
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social0JJ
scientist0NN
health0.191387734041787NN

Label: 10, with a score of: 0.441510689701682
WordScorePoS
support0NN
policy0.0848203862485078NN
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principle0.0977392570420122NN
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social0.101310366625194JJ
scientist0NN
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Engineers should move beyond their disciplines to evaluate alternatives and to effect policy changes toward sustainable development

Label: 17, with a score of: 0.465459449231649
WordScorePoS
move0NN
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development0.0469463607292177NN

Label: 9, with a score of: 0.430856748256336
WordScorePoS
move0NN
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Label: 10, with a score of: 0.394561117168738
WordScorePoS
move0NN
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policy0.0848203862485078NN
sustainable0.00138439773401932JJ
development0.0177346522508396NN

Label: 12, with a score of: 0.379074648639712
WordScorePoS
move0.0409791940864438NN
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policy0.0203215381458133NN
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They should develop partnerships with other design professionals economists and social environmental and physical scientists to arrive at environmentally sustainable and socially equitable solutions

Label: 17, with a score of: 0.786378957592703
WordScorePoS
develop0VB
partnership0.3914644359026NN
social0JJ
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scientist0NN
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This means that engineers along with other technical professionals should actively engage in the full life cycle of decision making processes including the interdisciplinary process of building the evaluation and decision framework and the infrastructure to realize the required sustainable future

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ANNEX ANNEX Data Requirements for Abbreviated Urban Metabolism Studies Quantity INFLOWS Food Water imports Water precipitation Groundwater abstraction Construction materials Fossil fuels by type Electricity Total incoming solar radiation Phosphorus PRODUCED Food Construction materials STOCKS Construction materials Phosphorus Landfill waste Construction demolition waste OUTFLOWS Exported landfill waste Incinerated waste Exported recyclables Wastewater Phosphorus SO NOx CO Volatile organics Particulates Methane Ozone Black carbon GCIF Required for GHG calculation √ √ √ Notes Standard climate data Primarily cement aggregates steel √ √ GCIF Global City Indicators Facility has upstream embodied greenhouse gas GHG emissions typically omitted from greenhouse gas calculations due to difficulty in estimation √ √ √ √ Standard climate data Example nutrient √ √ Cement and steel production In the building stock √ Accumulated √ √ Air emission plus accumulated mass √ √ √ √ Source Kennedy and Hoornweg

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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Material Flows in Cities The Urban Development and Resilience Unit of the World Bank has carried out Abbreviated Urban Metabolism Studies in number of cities and metropolitan areas including Rio de Janeiro Metro Manila São Paulo and Amman

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The format for presenting the material flow diagrams has not yet been standardized but some examples are included below

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All data are from national and sub national statistical agencies and local government units and annual values are shown

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FIG

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Abbreviated Urban Metabolism Diagram for Rio de Janeiro Brazil Source Gisela Campillo ANNEX FIG

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Abbreviated Urban Metabolism Diagram for Metro Manila the Philippines Source Artessa Saldivar Sali URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS FIG

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Abbreviated Urban Metabolism Diagram for São Paulo Brazil Source Gisela Campillo ANNEX FIG

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Abbreviated Urban Metabolism Diagram for Amman Jordan Source Lorraine Sugar URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX The World Largest Urban Areas as of Rank Urban Area Tokyo Mexico City Mumbai Bombay New York São Paulo Delhi Calcutta Jakarta Buenos Aires Dhaka Shanghai Los Angeles Karachi Lagos Rio de Janeiro Osaka Kobe Cairo Beijing Moscow Metro Manila Istanbul Paris Seoul Tianjin Chicago Lima Bogotá London Tehran Hong Kong Chennai Madras Bangalore Bangkok Dortmund Bochum Lahore Hyderabad Wuhan Baghdad Kinshasa Riyadh Santiago Miami Belo Horizonte Philadelphia St Petersburg Ahmadabad Madrid Toronto Ho Chi Minh City Chongqing Country Japan Mexico India USA Brazil India India Indonesia Argentina Bangladesh China USA Pakistan Nigeria Brazil Japan Egypt China Russia Philippines Turkey France South Korea China USA Peru Colombia UK Iran China India India Thailand Germany Pakistan India China Iraq Congo Dem

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Rep

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Saudi Arabia Chile USA Brazil USA Russia India Spain Canada Vietnam China World Bank Income Group Population in millions High income Upper middle income Lower middle income High income Upper middle income Lower middle income Lower middle income Lower middle income Upper middle income Low income Upper middle income High income Lower middle income Lower middle income Upper middle income High income Lower middle income Upper middle income Upper middle income Lower middle income Upper middle income High income High income Upper middle income High income Upper middle income Upper middle income High income Upper middle income High income Lower middle income Lower middle income Upper middle income High income Lower middle income Lower middle income Upper middle income Lower middle income Low income High income Upper middle income High income Upper middle income High income Upper middle income Lower middle income High income High income Lower middle income Upper middle income

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ANNEX Rank Urban Area Shenyang Dallas Fort Worth Pune Poona Khartoum Singapore Atlanta Sydney Barcelona Houston Chittagong Boston Washington DC Hanoi Yangon Bandung Detroit Jeddah Milan Guadalajara Surat Guangzhou Pôrto Alegre Casablanca Alexandria Frankfurt Wiesbaden Melbourne Ankara Abidjan Recife Monterrey Montréal Chengdu Phoenix Mesa Pusan Brasília Johannesburg Kabul Salvador Algiers San Francisco Oakland Düsseldorf Essen Fortaleza Medellín Berlin Pyongyang Caracas Xian Athens East Rand Ekurhuleni Cape Town Country China USA India Sudan Singapore USA Australia Spain USA Bangladesh USA USA Vietnam Myanmar Indonesia USA Saudi Arabia Italy Mexico India China Brazil Morocco Egypt Germany Australia Turkey Côte d'Ivoire Brazil Mexico Canada China USA Republic of Korea Brazil South Africa Afghanistan Brazil Algeria USA Germany Brazil Colombia Germany Korea Dem

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Rep

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Venezuela China Greece South Africa South Africa World Bank Income Group Population in millions Upper middle income High income Lower middle income Lower middle income High income High income High income High income High income Low income High income High income Lower middle income Low income Lower middle income High income High income High income Upper middle income Lower middle income Upper middle income Upper middle income Lower middle income Lower middle income High income High income Upper middle income Lower middle income Upper middle income Upper middle income High income Upper middle income High income High income Upper middle income Upper middle income Low Income Upper middle income Upper middle income High income High income Upper middle income Upper middle income High income Low Income Upper middle income Upper middle income High income Upper middle income Upper middle income

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Source The City Mayors Foundation http citymayors

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com statistics urban

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html URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Large Urban Areas Compendium Entries for Rio De Janeiro Singapore and Cape Town This annex provides samples of the Large Urban Areas data compendium

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Please visit the following website for the complete data compendium http go

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html GDP billions PriceWaterhouse Coopers Human Development Index Country

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CiƟes and Climate Change An Urgent Agenda GHG Emissions per GDP ktCO bn

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CiƟes and Climate Change An Urgent Agenda PM

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hƩp hdrstats

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undp

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html Up to date Local Agenda or equivalent

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N N UN Habitat State of the World's CiƟes naƟonal value for urban areas World Bank Doing Business Report SubnaƟonal Case Studies

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London School of Economics CiƟes Health and WellBeing

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London School of Economics CiƟes Health and WellBeing

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WHO Global Health Observatory naƟonal value WHO Global Health Observatory naƟonal value WHO Global Health Observatory naƟonal value UN PREVIEW Global Risk Data Plaƞorm Shelter Jobs Housing raƟo PopulaƟon living in slums Governance Voter parƟcipaƟon of eligible voters Time to start business days Debt service raƟo TransportaƟon Average commute Ɵme to work minutes High capacity public transit system km per populaƟon Light passenger transit system km per populaƟon Number of personal automobiles per capita Annual number of public transit trips per capita EducaƟon technology and innovaƟon EducaƟon Index Student teacher raƟo Students compleƟng primary educaƟon Students compleƟng secondary educaƟon Internet connecƟons per populaƟon New patents per per year Health Health Index Average life expectancy years Under age five mortality per live births Prevalence of HIV in adults aged to Deaths due to malaria per populaƟon Prevalence of tuberculosis per populaƟon In paƟent hospital beds per populaƟon Physicians per populaƟon Note Unavailable data to be provided by city or Global City Indicators Facility GCIF unless otherwise specified Koppen Climate ClassificaƟon System URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Estimated Multi Hazard Risk Index for the Largest Urban Areas This dataset includes an estimate of the risk induced by multiple hazards tropical cyclone flood and landslide induced by precipitation

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Label: 10, with a score of: 0.183000143158659
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Label: 13, with a score of: 0.176329689100769
WordScorePoS
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Label: 4, with a score of: 0.171024661601174
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Label: 8, with a score of: 0.0842009170249872
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The unit is the estimated risk index from low to extreme

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This product was designed by UNEP GRID Europe for the Global Assessment Report on Disaster Risk Reduction GAR

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Label: 3, with a score of: 0.327056958480818
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City Country Population Millions Risk Index Mexico City Mumbai Bombay Jakarta Buenos Aires Karachi Rio de Janeiro Cairo Beijing Istanbul Tehran Bangalore Dortmund Bochum Riyadh Belo Horizonte St Petersburg Singapore Sydney Barcelona Jidda Milan Alexandria Frankfurt Wiesbaden Chengdu Phoenix Mesa Brasília Johannesburg Salvador Algiers Fortaleza Berlin Athens East Rand Ekurhuleni Hyderabad Düsseldorf Essen São Paulo Washington DC Caracas Ahmadabad Porto Alegre Dallas Fort Worth London Abidjan Atlanta Osaka Kobe Mexico India Indonesia Argentina Pakistan Brazil Egypt China Turkey Iran India Germany Saudi Arabia Brazil Russia Singapore Australia Spain Saudi Arabia Italy Egypt Germany China USA Brazil South Africa Brazil Algeria Brazil Germany Greece South Africa India Germany Brazil USA Venezuela India Brazil USA UK CÐte Ivoire USA Japan

Label: 11, with a score of: 0.980534824249609
WordScorePoS
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Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Low Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate ANNEX City Country Population Millions Risk Index Shenyang Guadalajara Detroit Madrid Seoul Medellín Toronto Miami Philadelphia Xian Boston Santiago Cape Town Melbourne Chongqing Montreal Bandung Los Angeles New York San Francisco Oakland Chicago Casablanca Monterrey Guangzhou Tokyo Moscow Recife Kabul Lagos Ankara Ho Chi Minh City Lima Paris Houston Bangkok Bogotá Tianjin Hong Kong Lahore Baghdad Calcutta Pune Poona Pusan Yangon Khartoum Chennai Madras Shanghai Hanoi Pyongyang Delhi Surat Kinshasa Chittagong Metro Manila Wuhan Dhaka China Mexico USA Spain South Korea Colombia Canada USA USA China USA Chile South Africa Australia China Canada Indonesia USA USA USA USA Morocco Mexico China Japan Russia Brazil Afghanistan Nigeria Turkey Vietnam Peru France USA Thailand Colombia China China Pakistan Iraq India India Republic of Korea Myanmar Sudan India China Vietnam North Korea India India Congo Bangladesh Philippines China Bangladesh

Label: 11, with a score of: 0.987145182567473
WordScorePoS
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Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium High High High High High High High High High High High High High High Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Extreme Source UNEP GRID Europe http preview

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grid

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unep

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ch URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Urban Risk Assessment Review No

Label: 11, with a score of: 0.922261358762154
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urban0.27622591898666JJ
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Title Organization Year Methodology Scope Regions Covered STUDIES Port cities of over million population globally OECD Ranking Port Cities with High Exposure and Vulnerability to Climate Extremes Evaluated port cities of over million population with exposure to in year surge induced flood events

Label: 11, with a score of: 0.852642188702972
WordScorePoS
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Label: 1, with a score of: 0.437917530411858
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Label: 13, with a score of: 0.165948266506619
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The study looks at the exposure of population and assets in and those predicted in for different climate change scenarios

Label: 13, with a score of: 0.901498911594064
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Future predictions account for population growth urbanization ground subsidence and climatic changes

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Label: 11, with a score of: 0.50969225900278
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urbanization0.146277152173395NN

Label: 10, with a score of: 0.492546160601683
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Munich Re Megacities Megarisks The index is composed of three variables exposure to hazards vulnerability of the built environment and value of exposed property

Label: 1, with a score of: 0.912882990586594
WordScorePoS
exposure0.120266261535276NN
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Vulnerability is based on an estimation of the vulnerability of the predominant form of residential construction to hazards the standard of preparedness and safeguards including building regulations urban planning for specific hazards and flood protection as well as building density

Label: 11, with a score of: 0.610704771164681
WordScorePoS
vulnerability0NN
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urban0.27622591898666JJ
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density0.0731385760866977NN

Label: 16, with a score of: 0.36589932386857
WordScorePoS
vulnerability0NN
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plan0NN
protection0NN
density0NN

Label: 17, with a score of: 0.356420298544092
WordScorePoS
vulnerability0NN
base0.017987180284335NN
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regulation0.036961556179878NN
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plan0.0130185595639838NN
protection0NN
density0NN

Label: 1, with a score of: 0.480588529243754
WordScorePoS
vulnerability0.120266261535276NN
base0.0442083771593746NN
form0.0884167543187491NN
residential0JJ
hazard0NN
standard0NN
safeguard0VB
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building0NN
regulation0NN
urban0JJ
plan0NN
protection0.122839698738001NN
density0NN

The values of exposed property are estimated using the average values per household and the GDP for commerce and industry

Label: 9, with a score of: 0.502408509979173
WordScorePoS
value0NN
property0NN
estimate0.0221298700466866NN
average0NN
household0NN
gdp0NN
commerce0NN
industry0.181897200573757NN

Label: 10, with a score of: 0.44238120316186
WordScorePoS
value0NN
property0NN
estimate0NN
average0.146608885563018NN
household0.0330412182586772NN
gdp0NN
commerce0NN
industry0NN

Label: 13, with a score of: 0.407889321848024
WordScorePoS
value0NN
property0NN
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average0.1656430212089NN
household0NN
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commerce0NN
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Munich Re Group NatCat database was used to prepare natural hazard risk index for of the world largest over million population and most economically important cities based on city GDP as percentage of country GDP

Label: 11, with a score of: 0.950713814620785
WordScorePoS
natural0.0229054731708393JJ
hazard0NN
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world0.00626002771693937NN
largest0JJS
population0.0194583992186006NN
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city0.607697021770652NN
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The index analyzed large cities in low and middle income nations and large cities in high income nations

Label: 11, with a score of: 0.901788461632005
WordScorePoS
city0.607697021770652NN
middle0JJ
income0NN
nation0NN

Label: 10, with a score of: 0.305820039278424
WordScorePoS
city0NN
middle0JJ
income0.166475342664294NN
nation0.0396106020715138NN

GFDRR Henrike Brecht and WBG Exposure to cyclones and earthquakes in large cities may rise from million people in to

Label: 11, with a score of: 0.939240575257225
WordScorePoS
exposure0NN
city0.607697021770652NN
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billion people by Globally GFDRR Natural Hazards Unnatural Disasters City specific population projections to for this report are combined with geographic patterns of hazard events representative of the period

Label: 11, with a score of: 0.840393597078276
WordScorePoS
people0.0161552501392041NNS
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City rankings Comments Dhaka Chittagong Ningbo Lagos Khulna Kolkata Lomé Abidjan Haiphong Krung Thep Bangkok Surat Ho Chi Minh City Chennai Palembang Jakarta Mumbai Fuzhou Fujian Tianjin Xiamen Tokyo Los Angeles Osaka Kobe Kyoto New York Manila Quezon London Paris Chicago Mexico City Washington Baltimore Seoul Incheon Beijing Ruhr area Shanghai Moscow Bogotá Dhaka Mumbai Istanbul Teheran Calcutta Buenos Aires Lima Jakarta Karachi São Paulo Rio de Janeiro Cairo Delhi Lagos Highest hazard exposure growth in cities of the following regions in order South Asia cyclones Sub Saharan Africa East Asia earthquakes Latin America earthquakes City rankings developed on population exposure and asset exposure as well as population and asset exposure Disaster risk other than year surge induced flood events are not assessed

Label: 11, with a score of: 0.936892676103762
WordScorePoS
city0.607697021770652NN
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risk0.0631828116343571NN
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Label: 1, with a score of: 0.271597191909736
WordScorePoS
city0NN
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Label: 8, with a score of: 0.0835646417513798
WordScorePoS
city0NN
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The index is most heavily influenced by the exposure to asset values followed by hazard

Label: 1, with a score of: 0.702084153408747
WordScorePoS
heavily0RB
exposure0.120266261535276NN
asset0NN
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hazard0NN

Label: 16, with a score of: 0.473985087754433
WordScorePoS
heavily0RB
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asset0.0811931365363091NN
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Vulnerability plays lesser role

Label: 1, with a score of: 1
WordScorePoS
vulnerability0.120266261535276NN

Current city rankings are based on hot spot study

Label: 11, with a score of: 0.971821861649149
WordScorePoS
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The forthcoming study will include analysis on revised data

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ANNEX No

Label: 0, with a score of: 1

Title Organization Year Methodology Scope Regions Covered CITY RISK INDICES Combined scores were used to rank cities

Label: 11, with a score of: 0.961026229482922
WordScorePoS
organization0NN
region0NN
cover0NN
city0.607697021770652NN
risk0.0631828116343571NN

Label: 1, with a score of: 0.177542204897593
WordScorePoS
organization0NN
region0.184259548107002NN
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Compared selected coastal cities of Individual score given to the following Asia

Label: 11, with a score of: 0.90707423445404
WordScorePoS
compare0VB
coastal0JJ
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asia0IN

Label: 14, with a score of: 0.397030234268607
WordScorePoS
compare0VB
coastal0.265991560286306JJ
city0NN
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Exposure to temperature change precipitation change sea level rise extreme weather event

Label: 13, with a score of: 0.910318348186139
WordScorePoS
exposure0NN
temperature0.207053776511125NN
change0.402565546215053NN
sea0.16782457728855NN
level0.0415277415054218NN
rise0.120914181920259NN
extreme0.0335649154577099JJ
weather0.0671298309154199NN
event0.0414107553022249NN

Label: 1, with a score of: 0.317034418505802
WordScorePoS
exposure0.120266261535276NN
temperature0NN
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sea0NN
level0.0113874611101829NN
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extreme0.294526332970048JJ
weather0NN
event0.0908430554521382NN

Socioeconomic sensitivity population wealth per capita GDP contribution to national GDP

Label: 8, with a score of: 0.408858944591231
WordScorePoS
population0.0428212738652355NN
capita0.049270765107907NN
gdp0NN
contribution0NN
national0.0055149369084567JJ

Label: 10, with a score of: 0.397651272407619
WordScorePoS
population0.0860640399817412NN
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national0.00886732612541978JJ

Adaptive capacity existing preparedness response City rankings WWF Climate vulnerability Scorecard http assets

Label: 11, with a score of: 0.803941762308976
WordScorePoS
adaptive0JJ
capacity0.0112376090361851NN
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city0.607697021770652NN
climate0.0229054731708393NN
vulnerability0NN
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Label: 13, with a score of: 0.499726572150348
WordScorePoS
adaptive0.0548233071065585JJ
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response0.0548233071065585NN
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panda

Label: 0, with a score of: 1

org downloads mega cities report

Label: 11, with a score of: 0.993871610768121
WordScorePoS
city0.607697021770652NN
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pdf UNDP Disaster Risk Index http www

Label: 11, with a score of: 0.807962579233745
WordScorePoS
disaster0.157957029085893NN
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Label: 1, with a score of: 0.379595025787199
WordScorePoS
disaster0.0519476926891779NN
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undp

Label: 0, with a score of: 1

org cpr disred documents publications rdr english a

Label: 5, with a score of: 1
WordScorePoS
document0.0911713013253052NN

pdf The DRI enables the calculation of the average risk of death per country in large and mediumscale disasters associated with earthquakes tropical cyclones and floods based on data from to

Label: 3, with a score of: 0.558972844135985
WordScorePoS
enable0VB
average0NN
risk0.0703902516159815NN
death0.193573191943949NN
country0NN
disaster0NN
tropical0.0407408489778079JJ
base0.0149758277542848NN
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Label: 11, with a score of: 0.507221220586757
WordScorePoS
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death0.0315914058171786NN
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tropical0JJ
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Label: 13, with a score of: 0.379994994115507
WordScorePoS
enable0.0279982034978914VB
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Label: 10, with a score of: 0.256351210278284
WordScorePoS
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Stanford University Earthquake Disaster Risk Index http www

Label: 11, with a score of: 0.774355945596035
WordScorePoS
university0NN
disaster0.157957029085893NN
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Label: 1, with a score of: 0.363806038413024
WordScorePoS
university0NN
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stanford

Label: 0, with a score of: 1

edu group blume pdffiles Tech Reports TR Davidson

Label: 0, with a score of: 1

pdf Developed earthquake risk index and applied to selected cities

Label: 11, with a score of: 0.985915996589677
WordScorePoS
develop0VB
risk0.0631828116343571NN
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Index includes indicators on Hazard ground shaking collateral hazards Exposure population infrastructure economy Vulnerability population and infrastructure External context political and economic Emergency response and recovery planning planning resources mobility and access Selected cities from all parts of the world based on past exposure to earthquake IADB Risk Index at urban level http enet

Label: 11, with a score of: 0.694032554018417
WordScorePoS
include0VB
hazard0NN
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population0.0194583992186006NN
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level0.00692515656684925NN

Label: 9, with a score of: 0.271623753854481
WordScorePoS
include0VB
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population0NN
infrastructure0.192202909044225NN
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Label: 8, with a score of: 0.179197191611039
WordScorePoS
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Label: 1, with a score of: 0.360097641462073
WordScorePoS
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Label: 10, with a score of: 0.301693594356583
WordScorePoS
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iadb

Label: 0, with a score of: 1

org idbdocswebservices idbdocsInternet IADBPublicDoc

Label: 0, with a score of: 1

aspx

Label: 0, with a score of: 1

docnum IADB Indicators of Disaster Risk and Risk Management http idbdocs

Label: 11, with a score of: 0.775854612202797
WordScorePoS
disaster0.157957029085893NN
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Label: 1, with a score of: 0.342487788275414
WordScorePoS
disaster0.0519476926891779NN
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management0NN

Label: 3, with a score of: 0.337425748720741
WordScorePoS
disaster0NN
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iadb

Label: 0, with a score of: 1

org wsdocs getdocument

Label: 0, with a score of: 1

aspx Not operationalized at sub national level but methodology exists Not operationalized at sub national level but methodology exists The Disaster Deficit Local Disaster and Prevalent Available for Latin American countries Vulnerability indices DDI LDI and PVI are risk proxies that measure different factors that affect overall risk at the national level

Label: 11, with a score of: 0.550013327347555
WordScorePoS
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Label: 1, with a score of: 0.490906797495144
WordScorePoS
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IADB gas also developed the Risk Management Index the first systematic and consistent international technique developed to measure risk management performance

Label: 1, with a score of: 0.411134253858429
WordScorePoS
gas0NN
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Label: 13, with a score of: 0.346835341189382
WordScorePoS
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The index combines three sub indices capturing For ECA countries country exposure sensitivity and adaptive capacity

Label: 1, with a score of: 0.71056440559255
WordScorePoS
capture0NN
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exposure0.120266261535276NN
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The first exposure is based on an index measuring the strength of future climate change relative to today natural variability Baettig et al

Label: 13, with a score of: 0.825261108536416
WordScorePoS
exposure0NN
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Label: 1, with a score of: 0.362408798742062
WordScorePoS
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The index is available on country basis and includes both annual and seasonal temperature and precipitation indicators

Label: 13, with a score of: 0.956285255706742
WordScorePoS
country0NN
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It combines the number of additional hot dry and wet years hot dry and wet summers and hot dry and wet winters projected over the period relative to the period

Label: 0, with a score of: 1

This suggests that the countries most exposed to future climatic change are Russia Albania Turkey Armenia and to lesser extent Macedonia and Tajikistan

Label: 13, with a score of: 0.92295299931995
WordScorePoS
suggest0VB
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MAPS OF NATURAL HAZARDS AND CLIMATE CHANGE TREND ONLINE SOFTWARE AND DATABASE Presents global risks of two disaster related Available at country level outcomes mortality and economic losses

Label: 13, with a score of: 0.733387991586712
WordScorePoS
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Label: 11, with a score of: 0.316191071594839
WordScorePoS
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Label: 3, with a score of: 0.301363038501963
WordScorePoS
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Label: 8, with a score of: 0.137454925484747
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Estimated risk levels by combining hazard exposure with historical vulnerability for two indicators of elements at risk gridded population and GDP per unit area for six major natural hazards earthquakes volcanoes landslides floods drought and cyclones

Label: 1, with a score of: 0.617252949339406
WordScorePoS
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Three indicators were used

Label: 0, with a score of: 1

Disaster related mortality risks assessed for global gridded population

Label: 3, with a score of: 0.679849448180023
WordScorePoS
disaster0NN
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Label: 11, with a score of: 0.467103255491642
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Risks of total economic losses assessed for global gridded GDP per unit area

Label: 8, with a score of: 0.382703483641373
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risk0NN
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Label: 12, with a score of: 0.405124098361553
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Label: 1, with a score of: 0.3761287604331
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Label: 11, with a score of: 0.352650143745004
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Risks of economic losses expressed as proportion of the GDP per unit area for each grid cell ECA Climate Vulnerability Index http www

Label: 13, with a score of: 0.530817326536429
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Label: 8, with a score of: 0.258488945432125
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Label: 1, with a score of: 0.577869244740869
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worldbank

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org eca climate ECA CCA Full Report

Label: 13, with a score of: 0.871498551106476
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climate0.291881734282273NN
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Label: 7, with a score of: 0.343399994773272
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pdf Columbia University and World Bank Global Hot Spot study http www

Label: 2, with a score of: 0.623001321790679
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The Network of Social Studies in the Prevention of Disasters in Latin America Red de Estudios Sociales en Prevención de Desastres en América Latina LA RED conceptualized system of acquisition consultation and display of information about disasters of small medium and greater impact based on pre existing data newspaper sources and institutional reports in nine countries in Latin America

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Online platform and dataset for Latin America and parts of Asia and Africa Online software for disaster risk assessment Only for USA Online software for disaster risk assessment At country level ANNEX ANNEX Summary of The Next Frontier of Government In one third of the world population lived in cities

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Half century later the proportion had increased to one half and it is estimated that by billion people that is two thirds of the world population will live in cities

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In less than years most of the megacities emerging from that process will be located in developing countries see Figure

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Label: 16, with a score of: 0.178957202572324
WordScorePoS
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city0NN
future0NN
sustain0VB
type0NN
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level0.0610668815653026NN
production0NN
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public0.0285977592242194NN
service0NN
water0NN
transport0NN
electricity0NN
sanitation0NN
education0.0201317944152378NN
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Label: 4, with a score of: 0.147339726937338
WordScorePoS
projection0NN
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future0NN
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level0.0231525547935232NN
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key0NN
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water0NN
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There is however another side to this equation often overlooked

Label: 0, with a score of: 1

It relates to the emerging role of cities and of subnational entities generally to become global players as attractors of foreign investment competitiveness hubs and or platforms for the combination of local and international components of global production and supply chains

Label: 11, with a score of: 0.776164420314091
WordScorePoS
emerge0VB
city0.607697021770652NN
global0.00501208088147356JJ
foreign0JJ
investment0NN
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hub0.0731385760866977NN
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local0.0194583992186006JJ
international0JJ
production0NN
supply0NN
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Label: 12, with a score of: 0.422753599571277
WordScorePoS
emerge0VB
city0NN
global0.022306853314744JJ
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investment0NN
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local0.0144336476662975JJ
international0.0023966944644375JJ
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supply0.0703006268489394NN
chain0.0819583881728876NN

At the same time more and more governments around the world are seizing opportunities to move to government as way of enhancing the effectiveness and efficiency of their national public sectors in particular through outsourcing the production and delivery of public services to the private sector

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WordScorePoS
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Label: 5, with a score of: 0.470343726188055
WordScorePoS
time0NN
government0NN
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Label: 12, with a score of: 0.345854854431753
WordScorePoS
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Label: 9, with a score of: 0.328857889462247
WordScorePoS
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world0.00257642711761688NN
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This trend compounds another one by which central governments have been delegating an increasing number of their traditional responsibilities to subnational entities such as states regions municipalities or cities

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WordScorePoS
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Label: 1, with a score of: 0.261910990652962
WordScorePoS
central0JJ
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Many phrases and philosophies have been coined and formulated FIG

Label: 0, with a score of: 1

Projected Population Size of Mega cities in Source UN Habitat and authors calculations

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WordScorePoS
project0NN
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This article is summary of previously published chapter for the full list of references please see the original published article Lanvin and Lewin

Label: 0, with a score of: 1

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS to describe or justify such process including new federalism in the United States de centralization and de concentration in many European countries and even subsidiarity in the EU

Label: 11, with a score of: 0.727498267872167
WordScorePoS
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We are hence witnessing the rapid convergence and combination of three trends the growth in size and economic weight of local entities such as cities the increasing ability and will of governments to use information technologies and outsourcing to fulfill their tasks and serve their citizens better through government and the growing potential and obligation of local entities typically cities to act as global players designing and implementing their own policies and strategies to attract investment and carving out their share of benefits from the emerging global economy

Label: 11, with a score of: 0.703833706267519
WordScorePoS
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Label: 8, with a score of: 0.389298189862307
WordScorePoS
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Label: 17, with a score of: 0.315173439502574
WordScorePoS
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The Emergence of Local Global Players Both economic and urban literatures have long identified cities as key players in global competition and even as central engines in shaping and spreading globalization itself

Label: 11, with a score of: 0.907814612352163
WordScorePoS
local0.0194583992186006JJ
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Label: 8, with a score of: 0.184927461985717
WordScorePoS
local0.0214106369326177JJ
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Phrases such as global cities world cities or networked cities have been coined in the process

Label: 11, with a score of: 0.993942763448116
WordScorePoS
global0.00501208088147356JJ
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A growing number of local governments are emerging as local global players LGPs competing for international markets and investments

Label: 17, with a score of: 0.532656660933318
WordScorePoS
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Label: 2, with a score of: 0.44911245595631
WordScorePoS
grow0.0182713919265625VB
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Regularly international magazines publish rankings of cities worldwide according to cost of living and quality of life

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WordScorePoS
international0JJ
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Sometimes called cities Internet cities or knowledge cities new ready hubs seem to spring up around the world

Label: 11, with a score of: 0.989508403426363
WordScorePoS
city0.607697021770652NN
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Successful LGPs such as Singapore for example or Andhra Pradesh in India have combined superior levels of connectivity capable pool of human resources and an innovative private sector

Label: 17, with a score of: 0.609300561226833
WordScorePoS
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Label: 5, with a score of: 0.452010868878702
WordScorePoS
successful0JJ
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All of these can be furthered by local government policy however the quality and efficiency of local See Marcuse and Van Kempen

Label: 17, with a score of: 0.539412768750806
WordScorePoS
local0.0130185595639838JJ
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Label: 8, with a score of: 0.429905293033556
WordScorePoS
local0.0214106369326177JJ
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Label: 10, with a score of: 0.35262285907618
WordScorePoS
local0JJ
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See Sassen and Knox

Label: 0, with a score of: 1

See Townsend

Label: 0, with a score of: 1

efforts and governance that are key determinants of the success and the competitiveness of local global hubs are less often noticed or quantified

Label: 16, with a score of: 0.477147802566193
WordScorePoS
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Local government is emerging today as powerful tool by which such LGPs have enhanced and will continue to enhance their own competitiveness and that of their respective countries

Label: 17, with a score of: 0.537337415896274
WordScorePoS
local0.0130185595639838JJ
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Label: 12, with a score of: 0.421266979979567
WordScorePoS
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In many parts of the world building and promoting local champions of readiness is perceived as national priority by central governments

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WordScorePoS
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Label: 16, with a score of: 0.437838311065946
WordScorePoS
world0NN
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In countries as diverse as Tunisia Morocco Senegal Russia the United Arab Emirates Mexico Qatar or Saudi Arabia major plans are being designed and launched to build local versions of IT parks business process off shoring BPO centers and Internet knowledge cities in an effort to capture part of the increased foreign direct investment employment and economic growth that deepening globalization is expected to bring

Label: 11, with a score of: 0.624648449968465
WordScorePoS
country0NN
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Label: 8, with a score of: 0.432860567991285
WordScorePoS
country0NN
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Label: 9, with a score of: 0.349130416589246
WordScorePoS
country0NN
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It is increasingly recognized that it is not only national government led policy decision to support certain industry such as ICT over others as in the case of localized IT park or municipal decision to implement city strategy for global excellence

Label: 11, with a score of: 0.756872876339006
WordScorePoS
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Label: 9, with a score of: 0.281670763832707
WordScorePoS
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It is not only top down or supply driven approach that is causing local performance to gain relevance yet relatively little attention has been given so far to analyzing on globally comparative basis the role of government services in successful LGPs

Label: 11, with a score of: 0.509203636328633
WordScorePoS
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Label: 12, with a score of: 0.478816681758148
WordScorePoS
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How Do ICT and Government Benefit Local Global Players

Label: 17, with a score of: 0.625816935094445
WordScorePoS
government0.048933054487825NN
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Apart from typical national government services such as registrations customs taxation and elections it is local governments that have direct contact with citizens for multitude of services these local governments also attend to large number of citizens needs

Label: 0, with a score of: 1

Specific government ANNEX services are increasingly handled at the local rather than national level

Label: 16, with a score of: 0.427727372462338
WordScorePoS
government0NN
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This is the case for instance for small and medium sized enterprise SME registration vehicle and drivers licenses enrollment at educational institutions and vocational programs furthering human resources skills or professional authorizations and licenses for example for shops pharmacies and so on

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WordScorePoS
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Label: 16, with a score of: 0.395651797429987
WordScorePoS
medium0NN
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Label: 12, with a score of: 0.278478477578578
WordScorePoS
medium0NN
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The provision of increasing local government services contributes to readiness and competitiveness at the global level

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WordScorePoS
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global0.022306853314744JJ
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Label: 16, with a score of: 0.360952415994366
WordScorePoS
provision0.0658099748168827NN
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Label: 17, with a score of: 0.336865288984733
WordScorePoS
provision0NN
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global0.0201198688839505JJ
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Label: 14, with a score of: 0.307887467301575
WordScorePoS
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global0.0182280170776635JJ
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Tale of Many Cities Although analytical efforts have been made to describe local government initiatives and their good practices remarkably little attention has been granted to measuring the readiness of subnational spaces including cities

Label: 11, with a score of: 0.974093413171982
WordScorePoS
city0.607697021770652NN
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Two of the more useful attempts to measure urban performance or competitiveness are by Kaufmann et al

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WordScorePoS
measure0NN
urban0.27622591898666JJ
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Label: 10, with a score of: 0.338999574813867
WordScorePoS
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Label: 1, with a score of: 0.310127805777685
WordScorePoS
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at the World Bank and by Rutgers University in collaboration with South Korea Sungkyunkwan University in Seoul

Label: 2, with a score of: 0.508579886489535
WordScorePoS
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Kaufmann et al

Label: 0, with a score of: 1

include indicators that are vital to determining city level of readiness such as access to electricity telephone lines mobile telephones and Internet in schools

Label: 11, with a score of: 0.862839187968814
WordScorePoS
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vital0JJ
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city0.607697021770652NN
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internet0NN
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Label: 4, with a score of: 0.312315873706086
WordScorePoS
include0VB
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In contrast the Rutgers SKKU Governance Performance Index aims at ranking cities in terms of government performance leaving out some indicators of readiness such as access to ICT and the enabling environment

Label: 11, with a score of: 0.835846298819293
WordScorePoS
governance0NN
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aim0NN
city0.607697021770652NN
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government0NN
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access0.00626002771693937NN
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environment0.0268848280079943NN

Label: 17, with a score of: 0.341191410534716
WordScorePoS
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term0.149793392961411NN
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Combined with the World Economic Forum INSEAD Global Information Technology Report Networked Readiness Index NRI in which one can find significant number of key indicators that are relevant to the local level for example regulatory environment intensity of local competition firm level technology absorption and protection of property rights these three sets See Kaufmann et al

Label: 8, with a score of: 0.27013550521632
WordScorePoS
world0.00172202218602071NN
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local0.0214106369326177JJ
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Label: 1, with a score of: 0.376259602060717
WordScorePoS
world0.00257343720272548NN
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of data shed preliminary light onto the local government trends

Label: 17, with a score of: 0.893658190939327
WordScorePoS
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By comparing the overall city governance score of the Rutgers SKKU dataset with the overall country networked readiness score of the NRI one finds that the link between government performance of individual cities and the readiness of their respective countries is not straightforward see Figure

Label: 11, with a score of: 0.980021997820935
WordScorePoS
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In Figure the overall trend demonstrates that the majority of countries exhibit degree of local government performance the Rutgers SKKU Governance Performance Index on the vertical axis in line with what one could expect from their national networked readiness score the NRI on the horizontal axis

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WordScorePoS
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However several cities seem to be performing less successfully at the local level than their overall networked readiness would indicate for example Kuala Lumpur Stockholm and Helsinki

Label: 11, with a score of: 0.980920739729062
WordScorePoS
city0.607697021770652NN
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Others on the contrary perform better as local government hubs than the network readiness of their respective countries would suggest

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WordScorePoS
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This is the case for Tegucicalpa Honduras Ho Chi Minh City Vietnam Warsaw Poland Macao Hong Kong Shanghai China Sofia Bulgaria and Riga Latvia

Label: 11, with a score of: 0.997380969185671
WordScorePoS
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In particular three Eastern European cities in developing countries Sofia Warsaw and Riga are scoring close to or above on the city axis they are joined by three additional Eastern European cities Tallinn Bratislava and Budapest once the bar is lowered to scores or or higher

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WordScorePoS
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Clearly there is story to be told on city level successes in Eastern European local government

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WordScorePoS
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Indeed the picture becomes more interesting and somewhat different when one considers regional subsamples of the same data

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WordScorePoS
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Label: 9, with a score of: 0.471908251423744
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Because the overall sample which is based on the common subset of URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS FIG

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WordScorePoS
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City Government vs

Label: 11, with a score of: 0.994179944727542
WordScorePoS
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Overall Networked Readiness World Source NRI Rutgers SKKU Governance Performance Index and authors calculations

Label: 0, with a score of: 1

NRI and the Rutgers SKKU Governance Performance Index making total of countries is small such disaggregation cannot be pushed too far

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WordScorePoS
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Taking it to the level of broad regions North America South America and the Caribbean Western Europe Eastern and Central Europe Africa the Middle East and Asia and the Pacific few interesting observations emerge

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WordScorePoS
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Label: 3, with a score of: 0.567122515889488
WordScorePoS
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For Asia and the Pacific we find an ellipse that is flat see Figure indicating stronger correlation between overall network readiness and municipal government performance

Label: 0, with a score of: 1

However there are notable exceptions

Label: 0, with a score of: 1

Shanghai and Hong Kong as cities rank higher Shanghai at

Label: 11, with a score of: 0.997380969185671
WordScorePoS
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and Hong Kong at

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than the NRI score of China as whole

Label: 0, with a score of: 1

would suggest

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The same is true for Seoul the undisputed champion of the Rutgers SKKU index with score of

Label: 0, with a score of: 1

while Korea scores only

Label: 0, with a score of: 1

in this year NRI

Label: 0, with a score of: 1

The opposite story seems to affect Kuala Lumpur which as city performs more poorly than Malaysia as country

Label: 11, with a score of: 0.997380969185671
WordScorePoS
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At roughly the same level of overall networkreadiness the cities of Quezon City Philippines Jakarta Indonesia Ho Chi Minh City Vietnam and Macao Hong Kong and Shanghai China show stark differences in local government performance

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WordScorePoS
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To some extent the same can be said about Tokyo and Sydney which rank closely on both measures but when compared with similarly nationally networked Seoul they differ with markedly lower local government score

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WordScorePoS
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Moving to South America richer set of data offers interesting insights about the relation between city and country performance

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WordScorePoS
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Figure shows that the dispersion of South American countries along the spectrum of network readiness is broader than that of the corresponding countries along the axis of city government performance trans It must be noted here that for the purposes of this chapter Hong Kong Shanghai and Macao have been treated in the same manner governance indicators Rutgers SKKU data have been mapped against the country NRI rating for China

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This choice was made both for consistency reasons treating all Chinese cities in the same fashion but also because it befits the overall purpose of this section which is to identify cities for which local governance performance is above or below what the NRI performance of their respective national environments would suggest

Label: 11, with a score of: 0.964572010381288
WordScorePoS
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ANNEX lating visually in rather flat ellipse covering the cloud of points

Label: 0, with a score of: 1

Tegucicalpa Honduras and Sao Paolo Brazil clearly outperform their respective countries while Santiago Chile seems to tell the opposite story

Label: 0, with a score of: 1

The difference is striking between the respective city level government performances of cities such as regional high performer Sao Paolo on one hand and Mexico City on the other although both cities operate with very similar levels of overall networked readiness

Label: 11, with a score of: 0.990913242402533
WordScorePoS
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As might be expected Europe offers slightly complex picture even if one separates Western Europe from Eastern and Central Europe Figure FIG

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City Government vs

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WordScorePoS
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Overall Networked Readiness Asia and the Pacific Source NRI Rutgers SKKU Governance Performance Index and authors calculations

Label: 0, with a score of: 1

FIG

Label: 0, with a score of: 1

City Government vs

Label: 11, with a score of: 0.994179944727542
WordScorePoS
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government0NN

Overall Networked Readiness South America Source NRI Rutgers SKKU Governance Performance Index and authors calculations

Label: 0, with a score of: 1

URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS FIG

Label: 11, with a score of: 0.859742443451157
WordScorePoS
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City Government vs

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WordScorePoS
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Overall Networked Readiness Eastern and Western Europe Source NRI Rutgers SKKU Governance Performance Index and authors calculations

Label: 0, with a score of: 1

FIG

Label: 0, with a score of: 1

DE City Government vs

Label: 11, with a score of: 0.994179944727542
WordScorePoS
city0.607697021770652NN
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Overall Networked Readiness Africa Middle East and North America Source NRI Rutgers SKKU Governance Performance Index and authors calculations

Label: 0, with a score of: 1

A first conclusion is that the difference between old Europe and new Europe is much less visible from the point of view of cities performance than it is from that of overall network readiness

Label: 11, with a score of: 0.979390775537594
WordScorePoS
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At the national level Estonia the birthplace of Skype remains the network readiness champion among emerging European economies but most of the other Eastern European economies considered also compare well with the laggards of Western Europe for example the Czech Republic Hungary Lithuania Slovenia compare favorably with Cyprus Greece and Italy

Label: 0, with a score of: 1

However on the city scale Eastern Europe has number of superior performers including Bratislava Slovakia Prague Czech Republic Sofia Bulgaria and above all Warsaw Poland and Riga Latvia who are leaders in the European region as whole

Label: 11, with a score of: 0.908672725728654
WordScorePoS
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Label: 1, with a score of: 0.275518259628906
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ANNEX The government performance of those last three cities is clearly higher than their respective overall networked readiness levels would indicate

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WordScorePoS
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The citylevel dataset offers further insight into the leadership of Warsaw Prague Riga and Tallinn with regard to its subindices on usability content and service delivery

Label: 0, with a score of: 1

Notably no Eastern European city scored well in the privacy and security subindex however they performed well often being in the top in the other categories usability content and service delivery

Label: 11, with a score of: 0.944842322752052
WordScorePoS
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Finally the dataset used in this study offers only small set of cities for three regions two cities in North America three in the Middle East and four in Africa if one includes Cairo in Africa rather than in the Middle East sample that is not sufficiently large to make significant observations

Label: 11, with a score of: 0.970130211629988
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Label: 1, with a score of: 0.14707637852087
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One can notice however that in those three regions the correlation between the NRI and the RutgersSKKU index is strong see Figure

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WordScorePoS
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Label: 3, with a score of: 0.485387914112608
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Label: 9, with a score of: 0.410888880937161
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The Next Frontier Local Readiness The analysis above has led to three major conclusions ' Subnational economic spaces cities in particular have played central role in shaping the current wave of globalization

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WordScorePoS
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Label: 8, with a score of: 0.272489977745848
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The emergence of LGPs can be seen as revenge of geography whereby the benefits of the death of distance which have allowed international operators to invest produce and sell across global networks of cooperation have been combined with those of the physical proximity or congregation of local players for example ICT hubs in India or more complex combinations of talents such as in London City

Label: 11, with a score of: 0.894119872093511
WordScorePoS
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Label: 3, with a score of: 0.251811356921937
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' The dynamics of the ICT sector and of ICT infrastructure and services in general tend to reinforce the influence and roles of the local level in the overall process of globalization

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Label: 5, with a score of: 0.367555635890637
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The advent of short range telecommunications technologies such as WiFi or WiMAX combined with the regulatory space offered to broadband providers generally are allowing the emergence of new business models that provide information intensive services including government at the local level

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Label: 17, with a score of: 0.515705727904763
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In countries where most of the steps have been taken to establish government at the national level as is the case in many Latin American countries for instance possibilities for taking advantage of new advances in IT seem to be even more significant at the local and particularly municipal level

Label: 0, with a score of: 1

For the next few years and for all those reasons the local level can truly be seen as the next frontier of government on worldwide scale

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' The various regions of the world tell different stories about the respective abilities of national economies and cities to enhance their respective levels of network readiness and to use government as tool for competitiveness good governance and improvement of the quality of life of their citizens

Label: 11, with a score of: 0.681037886912751
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Label: 1, with a score of: 0.258872080526574
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Label: 7, with a score of: 0.391037900226276
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However they all show even if at varying degrees that the digital divide is less broad between cities than it is between countries

Label: 11, with a score of: 0.960034691228924
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Label: 13, with a score of: 0.259828211220313
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This results not only from the superior agility of smaller economic spaces to seize opportunities in rapidly changing environments but also from the fact that LGPs tend to network almost naturally with each other the result of common technical constraints for example international ports need to adopt common procedures and technical norms to accommodate certain types of vessels or deal with multi modal transport or of the emergence of standard practices in the ways in which global business is being carried out across national borders

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Label: 8, with a score of: 0.302403564746291
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In all regions some world cities emerge from the pack showing higher rates of readiness and government readiness than their respective countries

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Label: 1, with a score of: 0.346853136755191
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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Piloting in Sustainable Cities Building sustainable cities will require transformation over the coming four decades as significant as the Industrial Revolution

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WordScorePoS
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Label: 9, with a score of: 0.224552385057395
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This transformation will occur at the intersection of clean energy and ICT Rifkin

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Label: 12, with a score of: 0.266797025220558
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The Climate Group is calling it the Clean Revolution massive scale up of technologies solutions business models and governance to put the world on path toward low carbon sustainable future

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We will need to provide for billion or more people who will be living in cities in with an additional billion entering the middle class

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And we must do all this at just one tenth of the greenhouse gas emissions we produce today Climate Group

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Label: 7, with a score of: 0.447093589368873
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It is in cities that governance is needed to make this transition possible along with supporting political action at sub national and national levels

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Label: 16, with a score of: 0.301677645750678
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Cities and local authorities are hubs for innovation

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Cities are also prime sites for doing more with less and finding the benefits from increased concentration of people while avoiding negative externalities such as congestion loss of amenities higher prices for key services such as electricity and peak energy demand

Label: 11, with a score of: 0.694333511642346
WordScorePoS
city0.607697021770652NN
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people0.0161552501392041NNS
avoid0VB
congestion0.0731385760866977NN
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price0NN
key0NN
service0.0269743071558856NN
electricity0NN
energy0.0492535935240667NN
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Label: 7, with a score of: 0.594480360249135
WordScorePoS
city0NN
increase0.0162235147372888NN
concentration0NN
people0.0108156764915259NNS
avoid0VB
congestion0NN
loss0NN
price0NN
key0.0625160381065121NN
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electricity0.0625160381065121NN
energy0.494617163780726NN
demand0NN

Label: 12, with a score of: 0.179647826904106
WordScorePoS
city0NN
increase0.00958677785775002NN
concentration0NN
people0.0119834723221875NNS
avoid0VB
congestion0NN
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key0NN
service0.00666957305782837NN
electricity0NN
energy0.133960988253756NN
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How Do Cities Make the Clean Revolution Reality

Label: 11, with a score of: 0.973759983534074
WordScorePoS
city0.607697021770652NN
clean0JJ

Label: 7, with a score of: 0.200348575215455
WordScorePoS
city0NN
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The early stages of the Clean Revolution are already underway characterized by pilot projects that test new solutions to city challenges involve the private sector and test new ways to influence behavior before the city makes large investments in new infrastructure technologies or services

Label: 11, with a score of: 0.836280271348229
WordScorePoS
stage0NN
clean0JJ
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city0.607697021770652NN
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infrastructure0.0194583992186006NN
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service0.0269743071558856NN

Label: 9, with a score of: 0.337216495202586
WordScorePoS
stage0.0602029956716497NN
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Label: 7, with a score of: 0.269045511470605
WordScorePoS
stage0NN
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Pilots are taking place around the world in response to climate change and sustainability challenges

Label: 13, with a score of: 0.904892607347574
WordScorePoS
world0.00351929966767313NN
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Label: 7, with a score of: 0.32194570047943
WordScorePoS
world0.00261936653839306NN
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climate0.115011305411453NN
change0.105749654845006NN
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Case studies appear on websites and in technical papers in academic journals

Label: 4, with a score of: 0.836232239541842
WordScorePoS
study0NN
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Pilots or small scale rollouts of solutions that are phased to scale over time help city leaders and the private sector build confidence in new ways of meeting citizens needs

Label: 11, with a score of: 0.827568804283926
WordScorePoS
scale0NN
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city0.607697021770652NN
private0JJ
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build0VB

Label: 9, with a score of: 0.345351514672621
WordScorePoS
scale0.0368585655748969NN
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time0.0188543197850406NN
city0NN
private0.0368585655748969JJ
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If the city has made long term goals very clear pilots arise independently either from the private sector or other actors

Label: 11, with a score of: 0.860643366617492
WordScorePoS
city0.607697021770652NN
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Label: 17, with a score of: 0.404539569695438
WordScorePoS
city0NN
term0.149793392961411NN
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private0.0898760357768464JJ
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In other cases they arise directly from investment by the city or national government which may set up special funds for pilot projects

Label: 11, with a score of: 0.918763293829239
WordScorePoS
arise0VB
investment0NN
city0.607697021770652NN
national0.00501208088147356JJ
government0NN
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special0.0747035830159326JJ
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The European Commission regularly funds sustainable city solutions as part of Framework Programme FP research funding

Label: 11, with a score of: 0.919004020871771
WordScorePoS
commission0NN
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Stimulus packages around the world in the last four years have supported green growth solutions

Label: 8, with a score of: 0.704529380172397
WordScorePoS
world0.00172202218602071NN
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Label: 10, with a score of: 0.343398340306774
WordScorePoS
world0.00276879546803864NN
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Label: 13, with a score of: 0.312409835022242
WordScorePoS
world0.00351929966767313NN
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Singapore is investing billion in clean energy related projects in order to attract talent and solutions to Singapore

Label: 7, with a score of: 0.871757559951356
WordScorePoS
invest0VB
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Label: 12, with a score of: 0.293648856405829
WordScorePoS
invest0VB
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energy0.133960988253756NN
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The World Bank and other multilateral institutions fund climate change related mitigation actions many of which occur in cities

Label: 13, with a score of: 0.679645321209095
WordScorePoS
world0.00351929966767313NN
bank0NN
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Label: 11, with a score of: 0.613087483110463
WordScorePoS
world0.00626002771693937NN
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city0.607697021770652NN

Label: 16, with a score of: 0.225776078030936
WordScorePoS
world0NN
bank0NN
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Two key criteria for successful sustainable cities are the involvement of private actors such as NGOs and businesses and support for individual behavior change for instance through the provision of information on cost and energysaving measures

Label: 11, with a score of: 0.657805041388147
WordScorePoS
key0NN
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Label: 13, with a score of: 0.492777325644193
WordScorePoS
key0NN
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successful0JJ
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city0NN
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Private actors may provide funding independently or through public private partnerships they may provide new and innovative technologies specially developed or adapted from what is done elsewhere they can provide information and knowledge and help disseminate it to citizens

Label: 17, with a score of: 0.830638708471532
WordScorePoS
private0.0898760357768464JJ
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The main areas of city consumption that lead to unsustainable use of resources are electricity lighting appliances electronic devices building heating and cooling transportation and industry

Label: 11, with a score of: 0.780003029074493
WordScorePoS
city0.607697021770652NN
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unsustainable0JJ
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Label: 12, with a score of: 0.432948642380426
WordScorePoS
city0NN
consumption0.332151194779746NN
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heating0NN
transportation0.0332151194779746NN
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Label: 9, with a score of: 0.226299032416611
WordScorePoS
city0NN
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electricity0.030745604615229NN
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industry0.181897200573757NN

Label: 8, with a score of: 0.190622310431407
WordScorePoS
city0NN
consumption0.147812295323721NN
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electricity0NN
building0NN
heating0NN
transportation0NN
industry0NN

Label: 17, with a score of: 0.169755735745875
WordScorePoS
city0NN
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unsustainable0JJ
resource0.0375923735942499NN
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In each of these areas it is possible to replace high carbon technology with lower carbon version or provide alternative activities that result in lower carbon emissions

Label: 13, with a score of: 0.602318567468984
WordScorePoS
carbon0.0559964069957828NN
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emission0.19598742448524NN

Label: 7, with a score of: 0.564451205856172
WordScorePoS
carbon0.0625160381065121NN
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http cordis

Label: 0, with a score of: 1

europa

Label: 0, with a score of: 1

eu fp home en

Label: 0, with a score of: 1

html ANNEX City pilot projects may therefore be broadly differentiated into two areas

Label: 11, with a score of: 0.987730317679545
WordScorePoS
city0.607697021770652NN
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Replacing an existing technology with better more sustainable version of that technology

Label: 17, with a score of: 0.667337622055632
WordScorePoS
exist0.0599173571845643VB
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sustainable0.0115176738164035JJ

Label: 7, with a score of: 0.527934062617671
WordScorePoS
exist0VB
technology0.109914925284606NN
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Label: 9, with a score of: 0.392412683011009
WordScorePoS
exist0VB
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A classic example is incandescent lighting which can be replaced by much more efficient forms of lighting such as LEDs

Label: 16, with a score of: 0.716719877693924
WordScorePoS
form0.158049144643351NN

Label: 5, with a score of: 0.45592926525219
WordScorePoS
form0.100540298425703NN

Label: 1, with a score of: 0.400951523555705
WordScorePoS
form0.0884167543187491NN

Trying new service or modification to the existing service

Label: 11, with a score of: 0.503900936151103
WordScorePoS
service0.0269743071558856NN
exist0.04477822029736VB

Label: 7, with a score of: 0.460861317427144
WordScorePoS
service0.0451471467893167NN
exist0VB

Label: 1, with a score of: 0.45278033531408
WordScorePoS
service0.0443555132287109NN
exist0VB

Label: 17, with a score of: 0.305817688742241
WordScorePoS
service0NN
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In tackling transportation congestion challenges policy maker has the choice to encourage vehicles themselves to be more efficient such as with CAFE standards encourage reductions in the use of the same number of vehicles or remove the vehicles from the roads altogether

Label: 12, with a score of: 0.883208176983239
WordScorePoS
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The first approach necessitates that some of the vehicles provided by the private sector are more efficient than those currently on the road like the LED example above and that more of these efficient vehicles can be rapidly rolled out replacing existing vehicles

Label: 12, with a score of: 0.930290604318921
WordScorePoS
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This technological approach is the focus in much of the piloting work that private companies undertake in the laboratory and in the market

Label: 12, with a score of: 0.560070633117027
WordScorePoS
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Label: 9, with a score of: 0.517351952529477
WordScorePoS
technological0.104016094894625JJ
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Label: 5, with a score of: 0.308155678553533
WordScorePoS
technological0JJ
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The other two options for reducing the impact of transportation require changes either to the transportation system itself or to citizens behavior perhaps induced by providing information or pricing incentives for instance better information about public transportation or congestion charging

Label: 12, with a score of: 0.540514902307563
WordScorePoS
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public0.028867295332595NN
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Label: 11, with a score of: 0.521255817229813
WordScorePoS
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Awareness Availability Accessibility Affordability Acceptance Piloting New Technologies The Climate Group LightSavers programme is an excellent example of piloting to support the replacement of existing technology with more efficient alternatives

Label: 13, with a score of: 0.455383205345395
WordScorePoS
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alternative0NN

Label: 7, with a score of: 0.520742193416312
WordScorePoS
awareness0NN
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exist0VB
alternative0NN

Label: 17, with a score of: 0.510520689181547
WordScorePoS
awareness0NN
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When better lighting solution exists there are potentially many reasons why it is not adopted including low awareness lock in to long procurement process agency issues where investors won see the benefits of solutions and basic lack of awareness of the new solution track record in deployment

Label: 12, with a score of: 0.56812586011521
WordScorePoS
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In the case of LightSavers The Climate Group used technology diffusion framework adapted from Everett Rogers work Rogers

Label: 13, with a score of: 0.72149501072605
WordScorePoS
climate0.291881734282273NN
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Label: 7, with a score of: 0.505471477257094
WordScorePoS
climate0.115011305411453NN
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Called the As framework it helped to design process for supporting cities in trialling new solutions and developing confidence for those to be scaled up

Label: 11, with a score of: 0.914803851751435
WordScorePoS
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Technologists will learn more about their product when they move from laboratory testing to realworld conditions

Label: 4, with a score of: 0.744318887370713
WordScorePoS
learn0.244520809605025VB
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Label: 8, with a score of: 0.550170846034045
WordScorePoS
learn0VB
product0.0591643007226199NN
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Label: 12, with a score of: 0.310184811688387
WordScorePoS
learn0VB
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The LightSavers pilots were set up to support procurement managers of outdoor street lighting usually in the transportation departments of cities to design trials that could help them build their own knowledge of how the new LEDs would function under real outdoor conditions

Label: 11, with a score of: 0.845304392490028
WordScorePoS
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Label: 17, with a score of: 0.350543710296664
WordScorePoS
set0.0978661089756501NN
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It was then possible to assess at relatively low expense which products functioned best for http cordis

Label: 8, with a score of: 0.542293301926526
WordScorePoS
product0.0591643007226199NN
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Label: 17, with a score of: 0.503653915789053
WordScorePoS
product0.017987180284335NN
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europa

Label: 0, with a score of: 1

eu fp home en

Label: 0, with a score of: 1

html Are markets potential users and policy makers aware of the technology potential and the present stage of development

Label: 9, with a score of: 0.579073742604752
WordScorePoS
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Label: 17, with a score of: 0.553824473864008
WordScorePoS
market0.030649619577211NN
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Label: 10, with a score of: 0.316394831781186
WordScorePoS
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Is the learning from pilots being shared sufficiently with potential users

Label: 4, with a score of: 0.816041000213128
WordScorePoS
learn0.244520809605025VB
share0NN
potential0NN
user0NN

Label: 17, with a score of: 0.419022595390059
WordScorePoS
learn0VB
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What are the gaps to awareness

Label: 12, with a score of: 0.746421374075187
WordScorePoS
gap0NN
awareness0.0819583881728876NN

Label: 9, with a score of: 0.548288024623948
WordScorePoS
gap0.0602029956716497NN
awareness0NN

Label: 13, with a score of: 0.377141053689039
WordScorePoS
gap0NN
awareness0.0414107553022249NN

Is the technology available locally to pilot test and scale up significantly

Label: 9, with a score of: 0.546352615947321
WordScorePoS
technology0.0810848128688654NN
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Label: 7, with a score of: 0.509162173860679
WordScorePoS
technology0.109914925284606NN
scale0NN

Label: 17, with a score of: 0.508829117149822
WordScorePoS
technology0.109843026967392NN
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Do existing policies or market imperfections constrain the user from adopting solutions even when they are available

Label: 17, with a score of: 0.612494840463234
WordScorePoS
exist0.0599173571845643VB
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Label: 2, with a score of: 0.406547204661618
WordScorePoS
exist0VB
policy0NN
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Label: 10, with a score of: 0.350597872815831
WordScorePoS
exist0VB
policy0.0848203862485078NN
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user0NN
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Can the technology compete on price with the appropriate support

Label: 17, with a score of: 0.602484005032429
WordScorePoS
technology0.109843026967392NN
price0NN
support0.0451108483130998NN

Label: 7, with a score of: 0.50049602569923
WordScorePoS
technology0.109914925284606NN
price0NN
support0.0188084899376888NN

Label: 9, with a score of: 0.423167085138807
WordScorePoS
technology0.0810848128688654NN
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What is the business case for procurement

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Can innovative financing overcome lack of capital

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Is the new solution finally accepted as viable alternative to existing methods

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FIG

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The As Framework URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS the city in question and to build confidence in larger procurement as the next step

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technology with percent of the benefits going to other sectors Pelissie et al

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In trials around the world that replaced existing lighting with LED luminaires and tested the lighting quality using the same methodology the findings were The Climate Group partnerships to pilot new services emerged from our work on the enabling role of ICTs

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The Internet together with laptops printers mobile phones and the networks and data centres that support them are estimated to account for percent of global emissions

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However if used appropriately they may also help make existing activities more efficient or allow new options for services to become possible

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In the SMART report showed that percent of global emissions could be saved in through ICT enabled energy efficiency by making buildings transportation industrial processes and the electricity grid more efficient Climate Group

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' LED luminaires provide equivalent illumination to High Intensity Discharge HID at half the energy use with even better performance from smart controls

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' There is excellent lumen maintenance for some products

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' There is excellent color stability for most products

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' On site system effectiveness due to directionality of LED light accounts for much of the energy savings

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' Early indoor applications have strong business case

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In China and India in particular these early trials are leading to large procurements of LEDs that will significantly reduce emissions from outdoor lighting in those cities

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Piloting New Models or Modifying Existing Services The Internet and the devices that connect to it are changing our economy

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McKinsey recently found that the Internet contributes

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percent to GDP more than energy or agriculture and it has contributed percent of GDP growth in the last years dramatic rise from the previous decade

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It also contributes

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jobs for every one lost due to Final report LightSavers forthcoming For instance smart metering solutions provide many new capabilities beyond the existing dumb meters

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The benefits discussed from smart meters include their network effects otherwise known as the smart grid With dynamic information available about the last mile of electricity gas or water networks it is possible to better match energy supply with demand

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Citizens can in theory see real time feedback about their actions

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And utilities can manage increasing intermittent sources of power such as wind more effectively

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Cities have become the site for solution trials because it is in the city that transportation buildings and electrical systems intersect and therefore where much of the SMART potential can be realized

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As noted in Chapter of this report broadband digital infrastructure can interconnect people and city systems allowing cities and their residents to respond to changing circumstances nearly in real time

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ANNEX Cisco Connected Urban Development CUD program has conducted pilots to demonstrate the role of ICTs in saving greenhouse gas emissions such as smart work solutions that help people avoid travelling at peak traffic times and help employers increase occupancy in buildings

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Some of these ideas are beginning to take hold more widely

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In recent survey of cities new ICT enabled initiatives such as smart grids cycle rental schemes and real time traffic information provision are being rolled out in about one third of the cities and Arup

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' Objectives for each economic actor may not be aligned with social economic or environmental value for the city and citizens

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However there are number of reasons why these solutions are difficult to implement at scale as we found in the recent report Information Marketplaces Overcoming these barriers involves designing pilot projects that test demand for solutions in the market not only the better supply of technology

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The presence of ICT alone is not enough to ensure emissions savings these tools need to be applied to change processes or behavior if emissions are to be saved

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' Smart city plans that are technology led run the risk of compromising development plans

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Smart metering is case in point in U

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markets technology driven approach has led to backlash amongst consumers who do not see the benefits of energy savings that were promised

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The presence of smart meters alone does not deliver the benefits promised it is when critical mass of them are deployed and services are running on their data that consumers will see the value

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' The value of digital investments is not being clearly articulated for all stakeholders

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Cities may be unsure of the payback or may not possess mechanisms to pay for up front costs even if payback is certain in the long term

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The Climate Group partnered with CUD to bring learning from pilots to wider audience and transitioned the work to the SMART initiative http www

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theclimategroup

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org our news news cisco and theclimate group to develop new connected urban development alliance http www

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connectdurbandevelopment

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org Climate Group Arup and Accenture

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Information Marketplaces The New Economics of Cities

Label: 11, with a score of: 0.979165984194528
WordScorePoS
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' Cities are complex organizations and decisions that involve multiple departments can take time and can often be at odds with the sales cycles of companies

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WordScorePoS
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Label: 12, with a score of: 0.436577239130172
WordScorePoS
city0NN
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Procurement cycles for cities can take up to three years from initiation to sale which can prevent innovative and usually under resourced companies from participating in smart city development opportunities

Label: 11, with a score of: 0.930611096083236
WordScorePoS
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Therefore piloting approach is needed which tests the applications of the network not only whether the technology is working in the field as expected or to technical standards

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Label: 12, with a score of: 0.415224670525692
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Label: 7, with a score of: 0.355318879855842
WordScorePoS
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Label: 17, with a score of: 0.355086456192998
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As John Seely Brown and Paul Duguid argue in their book The Social Life of Information learning is demand driven and technologies are dependent upon social norms and contexts

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WordScorePoS
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Label: 9, with a score of: 0.358477311520994
WordScorePoS
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Learning is usually treated as supply side matter

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WordScorePoS
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… But learning is much more demand driven

Label: 4, with a score of: 0.951191161681671
WordScorePoS
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People learn in response to need … when they have need then if the resources for learning are available people learn effectively and quickly

Label: 4, with a score of: 0.945276045341931
WordScorePoS
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This network makes it possible to learn rapidly aggregating demand from numerous disparate individuals

Label: 4, with a score of: 0.945087853926213
WordScorePoS
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Pilots need to be designed to under http theclimategroup

Label: 0, with a score of: 1

org our news news twenty global citieslaunch technology award to improve the living standards of millioncitizens URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS stand how the network applied to particular challenge will alter behaviors what will be the potential uptake of services what new or existing economic actors will be involved and the business models or partnership models required to scale up the solution

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WordScorePoS
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Label: 11, with a score of: 0.41762179316117
WordScorePoS
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Label: 8, with a score of: 0.257537183769427
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Label: 9, with a score of: 0.340982069416718
WordScorePoS
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It is the outcome of these interactions with technology that will save emissions or provide low carbon alternatives to high carbon activities

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Label: 9, with a score of: 0.319628334659887
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An emerging model for demand led solution piloting is the challenge or prize approach

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Label: 7, with a score of: 0.366028533916686
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Label: 13, with a score of: 0.364416244379341
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Nobel Prizes have long rewarded innovation in science for society and more recently technology Prizes have galvanized individuals to invest in problems to which no one has yet found the answer

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WordScorePoS
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Label: 9, with a score of: 0.515716701192127
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Label: 8, with a score of: 0.322299262670976
WordScorePoS
innovation0.0821985033584295NN
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Though the Prize can be criticized for leading to duplication in effort with only one winner taking the prize when competitors should also be rewarded for their time and effort it does create urgency and process for rapid learning

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WordScorePoS
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Label: 9, with a score of: 0.49498494575574
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The Living Labs Global Award has applied the challenge model specifically to the city piloting context

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The award is designed around the insight that cities are reinventing the wheel

Label: 11, with a score of: 0.997380969185671
WordScorePoS
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They are struggling with challenges other cities have faced and are looking for home grown solutions

Label: 11, with a score of: 0.94697078235651
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Therefore the first step in becoming participating city in the Living Labs Global Award is to openly declare challenge and demonstrate willingness to look globally for products and solutions

Label: 11, with a score of: 0.941316421109286
WordScorePoS
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Once the city has announced its intention to solve its challenge the Living Labs Global Award process takes the city on journey of discovery and evaluation of solutions that have been implemented elsewhere

Label: 11, with a score of: 0.971646948464968
WordScorePoS
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The city appoints jury which will choose in an appropriate solution for that city not necessarily the most technically advanced solution

Label: 11, with a score of: 0.983137091467236
WordScorePoS
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citizens globally

Label: 0, with a score of: 1

Cities will award the winning solutions providers with the potential to develop pilot projects in their city

Label: 11, with a score of: 0.985968575483222
WordScorePoS
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The Living Labs Global model has been designed for cities seeking smart or intelligent solutions and not all are directly related to sustainability or climate change

Label: 13, with a score of: 0.695843843714846
WordScorePoS
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Label: 11, with a score of: 0.601549596671261
WordScorePoS
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But many are Lagos is looking for inexpensive smart homes that can be deployed rapidly and help the city provide better services to residents Santiago de Chile is looking for innovative parking solutions to reduce environmental impact from congestion

Label: 11, with a score of: 0.899772226768111
WordScorePoS
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Key principles of the approach that may be replicated in other piloting schemes are as follows ' Start from the demand side city challenge leads the process

Label: 11, with a score of: 0.910034718959121
WordScorePoS
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' Help companies identify likely prospects By identifying cities ready to tackle challenge solutions providers can more quickly identify which of the local authorities are likely to be interested in solution

Label: 11, with a score of: 0.895394806474902
WordScorePoS
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' Provide pre procurement learning process for cities Cities will see where the solution has been implemented before thereby providing new options for cities to choose solutions they may never have envisioned ' Shorten the solution providers time to market Procurement can take months to years and slow citizens access to new services

Label: 11, with a score of: 0.92762653773472
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Label: 4, with a score of: 0.129993968630506
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By raising awareness of the solution in the context of city challenge it is possible to accelerate the solution diffusion curve

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Label: 13, with a score of: 0.382649385087792
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llga

Label: 0, with a score of: 1

org The Climate Group is partnering with Living Labs Global Award to bring climate and energy expertise to the process http www

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Label: 13, with a score of: 0.599908359677005
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Label: 12, with a score of: 0.147949059227128
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theclimategroup

Label: 0, with a score of: 1

org our news news twenty global cities launch technologyaward to improve the living standards of million citizens In the third year of the prize cities have announced challenges that could reach million ANNEX ' Encourage place based innovation Allow the city and company to work together defining pilot that is geographical

Label: 11, with a score of: 0.965755437262361
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When new service is tested it is important to test the business model as much as the solution itself

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Label: 13, with a score of: 0.355567902126821
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Therefore the pilot may need to occur not simply at solution level but at neighbourhood level

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Label: 16, with a score of: 0.544495136299151
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One of last year Living Labs Global Award winners parking solution for Helsinki is one example of new way of thinking about parking directed parking

Label: 0, with a score of: 1

It is scalable to other cities and has the potential to save emissions and time all while providing revenue for citizens businesses and the city

Label: 11, with a score of: 0.956000857742059
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Label: 13, with a score of: 0.187365398593713
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The platform creates marketplace for parking spaces that lets individuals sell their spots if they are away or would let restaurants offer parking along with dinner reservations

Label: 0, with a score of: 1

Cities can calculate their earnings from better access to public parking spots or the emissions saved from vehicles that go straight to parking spot instead of circling for many polluting minutes

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Label: 12, with a score of: 0.429780539829821
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Label: 13, with a score of: 0.235374659550919
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While this solution relies on range of technologies the network infrastructure mobile phones billing and booking system and data applications the service success requires business model that provides incentives for people to change their behavior

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Label: 13, with a score of: 0.531697425909469
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The pilot project will need to test the business model in the city context

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The Living Labs Global Award process demonstrates how cities can attract innovative climate and sustainability solutions that are contextual demand driven and use the power of the network to hasten the learning process

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Label: 4, with a score of: 0.283788149213355
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Dennis Frenchman Michael Joroff and Allison Albericci

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Smart Cities as Engines of Sustainable Growth

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Label: 8, with a score of: 0.321648686744335
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Washington DC World Bank Institute

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SpotScout Beta http www

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spotscout

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com scout Achieving Outcomes Scale Up of Pilots Many challenges to sustainability and green growth in cities remain even if successful pilots are underway

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Investors are not always the beneficiaries of solutions as we see in buildings where owners do not manage the day to day operations

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Label: 17, with a score of: 0.245858443230055
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Label: 16, with a score of: 0.33228667592271
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Lack of skills or the need to retrain as new technologies and solutions emerge has to be considered early or solutions will remain in the nascent stage

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technology0.0182963616752599NN
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stage0NN

Label: 9, with a score of: 0.425368892322997
WordScorePoS
lack0.0565629593551219NN
skill0NN
technology0.0810848128688654NN
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Changes in behavior are difficult to predict and infrastructure choices may lock in highcarbon behaviors for years to come

Label: 9, with a score of: 0.654459383680829
WordScorePoS
predict0VB
infrastructure0.192202909044225NN
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Label: 7, with a score of: 0.638609602312643
WordScorePoS
predict0VB
infrastructure0.0651354046449052NN
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What both the piloting approaches above have in common is that group of cities are piloting at the same time

Label: 11, with a score of: 0.978982002686113
WordScorePoS
approach0NN
common0.055245183797332JJ
city0.607697021770652NN
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In each case sharing the learning is key part of bridging information gaps and building confidence in expanding solutions to more citizens

Label: 4, with a score of: 0.557742166853017
WordScorePoS
share0NN
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Label: 17, with a score of: 0.473455264762113
WordScorePoS
share0.0766240489430275NN
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information0.0219686053934784NN
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Label: 9, with a score of: 0.385459288945225
WordScorePoS
share0.0377086395700813NN
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In the case of the LightSavers trials cities that pilot in parallel can share technical knowledge and compare results in different cities

Label: 11, with a score of: 0.963171241127195
WordScorePoS
city0.607697021770652NN
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Certain luminaires may work better in certain climates

Label: 13, with a score of: 0.909342631926563
WordScorePoS
climate0.291881734282273NN

Label: 7, with a score of: 0.358311846478955
WordScorePoS
climate0.115011305411453NN

Particular configurations of street lighting may lend themselves better to one product or another

Label: 8, with a score of: 0.694131471568362
WordScorePoS
product0.0591643007226199NN

In the case of Living Labs Global the concentration of city challenges generates huge interest amongst service providers and allows each city to benefit from the awareness raised about the award process

Label: 11, with a score of: 0.954352342706212
WordScorePoS
live0.0269743071558856VB
global0.00501208088147356JJ
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Living Labs Global is also able to research multiple solutions at once and to generate independent evaluation data across thousands of solutions that can be used by all the cities essentially creating global marketplace for solutions

Label: 11, with a score of: 0.756807997790938
WordScorePoS
live0.0269743071558856VB
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Label: 9, with a score of: 0.357513170483482
WordScorePoS
live0.00740117451847634VB
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Cities are the laboratories of the transition to Clean Revolution

Label: 11, with a score of: 0.973759983534074
WordScorePoS
city0.607697021770652NN
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Label: 7, with a score of: 0.200348575215455
WordScorePoS
city0NN
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Innovation and pilot project scale up will require significant investment and changes in governance and financing that we have only begun to grapple with

Label: 9, with a score of: 0.562457411685773
WordScorePoS
innovation0.122982418460916NN
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Label: 8, with a score of: 0.53259572031619
WordScorePoS
innovation0.0821985033584295NN
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require0.104282800328615VB
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Label: 17, with a score of: 0.37171214045937
WordScorePoS
innovation0.0499801157438617NN
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The successful transition is inextricably linked with governance institutional and organisational change to ensure that learning is shared pilots are not repeated unnecessarily and scaling the solutions is possible

Label: 13, with a score of: 0.764663561866378
WordScorePoS
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institutional0.0828215106044499JJ
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Label: 4, with a score of: 0.392519464032783
WordScorePoS
successful0JJ
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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX The Role of Technology in City Master Planning Technology is beginning to transform the way cities are structured the way people communicate and work and the way resources are managed yet within cities technology is rarely adopted in fully integrated and strategically designed manner

Label: 11, with a score of: 0.917935849909712
WordScorePoS
urban0.27622591898666JJ
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knowledge0NN
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The traditional approach to city planning has focused on the physical today additional planning around the city digital infrastructure is required to enable long term sustainability

Label: 11, with a score of: 0.91862554869495
WordScorePoS
traditional0JJ
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city0.607697021770652NN
plan0.0583751976558017NN
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additional0JJ
digital0JJ
infrastructure0.0194583992186006NN
require0VB
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Label: 17, with a score of: 0.246559617059921
WordScorePoS
traditional0JJ
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term0.149793392961411NN
sustainability0.036961556179878NN

Label: 9, with a score of: 0.160340132997648
WordScorePoS
traditional0JJ
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Defining the city digital and ICT requirements such as high speed broadband connectivity and open application architecture alongside the city physical blueprint for example land use zoning and transport planning and economic planning for example industrial sector strategy and taxation strategy will enable more efficient integrated services to be provided to citizens

Label: 11, with a score of: 0.892791696906174
WordScorePoS
city0.607697021770652NN
digital0JJ
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sector0NN
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enable0.0373517915079663VB
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Label: 9, with a score of: 0.252035530333478
WordScorePoS
city0NN
digital0JJ
land0NN
transport0.030745604615229NN
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strategy0NN
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service0.0148023490369527NN

Label: 8, with a score of: 0.146053211358389
WordScorePoS
city0NN
digital0JJ
land0NN
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economic0.128463821595706JJ
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Such multi disciplinary approach to urban planning is practiced by the business and technology consulting firm Accenture Figure which focuses the physical digital economic master Economic Masterplan FIG

Label: 11, with a score of: 0.513299753531482
WordScorePoS
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economic0.0389167984372011JJ

Label: 8, with a score of: 0.340110541276468
WordScorePoS
multus0NN
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urban0JJ
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technology0NN
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focus0.0410992516792148NN
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economic0.128463821595706JJ

Label: 9, with a score of: 0.373231528191036
WordScorePoS
multus0NN
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business0.0909486002868787NN
technology0.0810848128688654NN
figure0.0602029956716497NN
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digital0JJ
economic0.0320338181740375JJ

Label: 10, with a score of: 0.362669537066502
WordScorePoS
multus0NN
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urban0.0488696285210061JJ
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digital0JJ
economic0.103276847978089JJ

Label: 17, with a score of: 0.307738882699094
WordScorePoS
multus0.0978661089756501NN
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digital0.048933054487825JJ
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Label: 12, with a score of: 0.304208639210288
WordScorePoS
multus0NN
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An Integrated Approach to City Master Planning Service Delivery Physical Masterplan Digital Masterplan planning of city around the services required to meet the needs of its citizens and businesses

Label: 11, with a score of: 0.954537588377449
WordScorePoS
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By planning and deploying technology at the city level managers can realize economies of scale across departmental silos such as energy buildings and mobility

Label: 11, with a score of: 0.68136931955731
WordScorePoS
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energy0.0492535935240667NN
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Label: 7, with a score of: 0.570303698094584
WordScorePoS
plan0NN
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energy0.494617163780726NN
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Label: 17, with a score of: 0.296347605399823
WordScorePoS
plan0.0130185595639838NN
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energy0.0109843026967392NN
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Label: 12, with a score of: 0.13126427031789
WordScorePoS
plan0.0144336476662975NN
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level0.00513687014008325NN
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energy0.133960988253756NN
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For example smart grids are bringing together our energy and telecommunication grids and electric vehicles are connecting our transport systems with our energy networks making both energy and transport data more accessible

Label: 7, with a score of: 0.849415267162279
WordScorePoS
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energy0.494617163780726NN
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Label: 12, with a score of: 0.385998086860572
WordScorePoS
bring0VB
energy0.133960988253756NN
vehicle0.217007869186068NN
transport0.0277064208763707NN
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In addition technology enables city to provide new cross industry services

Label: 11, with a score of: 0.834229391201093
WordScorePoS
addition0NN
technology0NN
enable0.0373517915079663VB
city0.607697021770652NN
provide0.0269743071558856VB
industry0NN
service0.0269743071558856NN

Label: 9, with a score of: 0.384348561275059
WordScorePoS
addition0.0368585655748969NN
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In Singapore for example rainfall often comes in intense localized downpours which increases congestion in the city and makes it very difficult to find taxi

Label: 11, with a score of: 0.994989968572172
WordScorePoS
increase0.00323105002784081NN
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The Massachusetts Institute of Technology combined data on weather forecasts GPS taxi locations and mobile phone cell propagation to understand in real time how rainstorms will affect the city and to co locate taxis to the high demand areas

Label: 11, with a score of: 0.833939368830785
WordScorePoS
technology0NN
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Label: 9, with a score of: 0.324358352310981
WordScorePoS
technology0.0810848128688654NN
datum0.0454743001434393NN
weather0NN
location0NN
phone0.0602029956716497NN
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city0NN
demand0.030745604615229NN

Label: 17, with a score of: 0.319331857595855
WordScorePoS
technology0.109843026967392NN
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location0.048933054487825NN
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By using existing technologies in an integrated fashion the city has been able to provide useful new service to citizens and to better plan its resources and citizens are able to get cab even when it raining

Label: 11, with a score of: 0.885273907424479
WordScorePoS
exist0.04477822029736VB
technology0NN
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To realize the full potential of technology cities need to adopt combination of hard and soft infrastructure

Label: 11, with a score of: 0.807816578019741
WordScorePoS
realize0VB
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technology0NN
city0.607697021770652NN
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infrastructure0.0194583992186006NN

Label: 9, with a score of: 0.434325549354226
WordScorePoS
realize0VB
full0.030745604615229JJ
potential0.0454743001434393NN
technology0.0810848128688654NN
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infrastructure0.192202909044225NN

Hard infrastructure includes the city physical ICT assets data center capacity smart grids connectivity and bandwidth software and data visualizations and soft infrastructure involves tools to manage these assets business and governance models citizen engagement and strategic focus on ICT

Label: 11, with a score of: 0.65617799438872
WordScorePoS
infrastructure0.0194583992186006NN
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capacity0.0112376090361851NN
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focus0.0373517915079663NN

Label: 9, with a score of: 0.563533304335033
WordScorePoS
infrastructure0.192202909044225NN
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Cities today are already making investments in hard infrastructure such as smart transport and smart grid projects to drive http senseable

Label: 11, with a score of: 0.891736942110113
WordScorePoS
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Label: 9, with a score of: 0.331174226495092
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mit

Label: 0, with a score of: 1

edu livesingapore ANNEX economic and environmental benefits

Label: 8, with a score of: 0.531465595036476
WordScorePoS
economic0.128463821595706JJ
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Label: 1, with a score of: 0.471448754683491
WordScorePoS
economic0.0959899298575892JJ
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Label: 10, with a score of: 0.427264974545966
WordScorePoS
economic0.103276847978089JJ
environmental0.0237821918091848JJ

Label: 12, with a score of: 0.346791338842438
WordScorePoS
economic0.0433009429988926JJ
environmental0.0598270388037045JJ

Label: 11, with a score of: 0.311679323793097
WordScorePoS
economic0.0389167984372011JJ
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However their approach tends to be fragmented across city departments

Label: 11, with a score of: 0.993450567531736
WordScorePoS
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On the soft infrastructure front local and national governments need to understand the role they want to play in relation to the city data and digital assets and they need to establish leadership capabilities in the form of Chief Information Office CIO for example to set the strategic ICT direction for the city and to put in place appropriate frameworks and incentives to enable the digital economy to flourish

Label: 11, with a score of: 0.808896389477677
WordScorePoS
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Label: 9, with a score of: 0.246038266929031
WordScorePoS
infrastructure0.192202909044225NN
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Label: 16, with a score of: 0.17997732705088
WordScorePoS
infrastructure0NN
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Label: 17, with a score of: 0.377853618593021
WordScorePoS
infrastructure0.0130185595639838NN
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Placing an equal emphasis on soft and hard infrastructure will enable cities to create long term socioeconomic and environmental value

Label: 11, with a score of: 0.836744202781109
WordScorePoS
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environmental0.0537696560159885JJ

Label: 9, with a score of: 0.297197721518217
WordScorePoS
equal0JJ
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Label: 17, with a score of: 0.237445076178344
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The framework below Figure shows the different levels of implementation that cities can achieve in terms of their hard and soft infrastructure it highlights that while the smart city concept is becoming well known the vast majority of cities are far from implementing the infrastructure required to reap the full sustainability benefits of smart technology

Label: 11, with a score of: 0.885927900041352
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Label: 9, with a score of: 0.274614452333888
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technology0.0810848128688654NN

Label: 17, with a score of: 0.203070593940915
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sustainability0.036961556179878NN
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Setting single set of metrics at the city level will also enable cities to evaluate the success of sustainability initiatives on like for like basis

Label: 11, with a score of: 0.944067331314234
WordScorePoS
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For instance smart grid projects are measured by reduction in energy losses and efficiency gains and variable road pricing schemes are measured by reduced traffic congestion

Label: 7, with a score of: 0.622282802765615
WordScorePoS
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WordScorePoS
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WordScorePoS
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While the value of each project can readily be assessed at the departmental level it is less easy to understand the contribution of the project to the city overarching objectives

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For example how would city compare the relative value contributed by smart grid with FIG

Label: 11, with a score of: 0.980667824117787
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Framework for Smarter City Source Reprinted from The Climate Group Accenture Arup and The University of Nottingham

Label: 11, with a score of: 0.844753116012878
WordScorePoS
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Label: 13, with a score of: 0.433200229149742
WordScorePoS
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Information Marketplaces The New Economics of Cities

Label: 11, with a score of: 0.979165984194528
WordScorePoS
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URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS that contributed by variable road pricing toward its city wide aims of economic development livability and environmental sustainability

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Label: 8, with a score of: 0.175455521469369
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Such questions present challenge to city leaders who need to make capital allocation decisions across portfolio of initiatives

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For the value of initiatives to be effectively compared common suite of metrics needs to be developed that ties the performance of individual initiatives to the city long term strategic aims

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Label: 17, with a score of: 0.316789793972088
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Label: 7, with a score of: 0.331014904503358
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A single city scorecard based on specific objectives will enable the city to understand the relative value of different initiatives based on how well each delivers on the city overall strategy

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FIG

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Measuring Urban Sustainability Initiatives Against Common Set of Metrics Source Reprinted from The Climate Group Accenture Arup and The University of Nottingham

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Label: 7, with a score of: 0.362203152264309
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Information Marketplaces The New Economics of Cities

Label: 11, with a score of: 0.979165984194528
WordScorePoS
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ANNEX ANNEX Eco Cities Ecological Cities as Economic Cities Eco Cities is an initiative launched by the World Bank in as an integral part of its Urban and Local Government Strategy to help cities in developing countries achieve greater ecological and economic sustainability

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Label: 8, with a score of: 0.0930962281315487
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Ecological cities enhance the well being of citizens and society through integrated urban planning and management that fully harnesses the benefits of ecological systems and that protects and nurtures these assets for future generations

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Economic cities create value and opportunities for citizens businesses and society by efficiently using all tangible and intangible assets and enabling productive inclusive and sustainable economic activity

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What is an Eco City

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An Eco city builds on the synergy and interdependence of ecological and economic sustainability and their fundamental ability to reinforce each other in the urban context

Label: 11, with a score of: 0.909723880374818
WordScorePoS
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Label: 8, with a score of: 0.186561948605175
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Innovative cities in both the developed and the developing world have demonstrated that with the appropriate strategic approach they can enhance their resource efficiency realizing the same economic value from much smaller and renewable resource base while simultaneously reducing harmful pollution and unnecessary waste

Label: 11, with a score of: 0.714822841636899
WordScorePoS
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Label: 12, with a score of: 0.37853815588065
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Label: 7, with a score of: 0.212798432621289
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Label: 8, with a score of: 0.146521881131287
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By doing so they have improved the quality of life of their citizens enhanced their economic competitiveness and resilience strengthened their fiscal capacity and created an enduring culture of sustainability

Label: 8, with a score of: 0.488013676908551
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Label: 12, with a score of: 0.378100679869277
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Label: 1, with a score of: 0.362838574593953
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Label: 11, with a score of: 0.350592473223442
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Many of their interventions have also provided significant benefits to the poor

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Urban sustainability of this kind is powerful and enduring investment that will pay compounding dividends

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Label: 17, with a score of: 0.301130205092308
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In fast Eco Cities Synopsis http siteresources

Label: 11, with a score of: 0.997380969185671
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worldbank

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org INTURBANDEVELOPMENT Resources Eco Cities synopsis

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pdf paced and uncertain global economy cities that adopt such an integrated approach are more likely to survive shocks attract businesses manage costs and prosper

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The Eco Cities Initiative was developed to enable cities in developing countries to realize this value and to and take on more rewarding and sustainable growth trajectory while the window of opportunity is still open to them

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Unique Features of the Eco Cities Initiative The Eco Cities Initiative provides cities with an analytical and operational framework that can be applied and contextualized to the particular challenges of each city

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The framework also includes methods and tools that make it easier for cities to adopt the Eco approach as part of their city planning development and management

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The Eco Cities Initiative also assists cities in developing countries to gain access to financial resources needed for strategic urban infrastructure investments

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Label: 9, with a score of: 0.15549359379837
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Another important feature of Eco is its bottom up approach

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Label: 12, with a score of: 0.543630559549152
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Label: 14, with a score of: 0.533072432059364
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Innovative best practice cities around the world have demonstrated how ecological and economic progress can go hand in hand

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Eco elements systematically build on these global best practices

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Label: 17, with a score of: 0.406924474463791
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Label: 1, with a score of: 0.356537024349214
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How The Eco Cities Initiative Works The Eco Cities Initiative works through the application of an analytical and operational framework that helps cities systematically achieve positive results

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As framework it provides point of departure and needs to be customized to the particular context of each city

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After careful assessment of cities that have benefited tremendously from this sort of approach URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS as well as detailed look at the major challenges that have prevented similar achievements in most other cities the framework has been structured around four key principles that were found to be integral to lasting success

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These principles are the foundation of the Eco initiative

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Label: 7, with a score of: 0.529174648793249
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Each principle is widely applicable critical to success and frequently ignored or underappreciated ANNEX The four principles are interrelated and mutually supportive

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Without strong city based approach it is very difficult to fully engage key stakeholders through an expanded platform for collaborative design and decision making

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And without this expanded platform it is difficult to explore creative new approaches to the design and management of integrated systems and to coordinate policies to implement through the one system approach

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Prioritization sequencing and effectiveness of investments in sustainability and resiliency will be greatly enhanced by appreciating the city as one system and expanding the platform of collaboration

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set of core elements have been derived through these principles

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Label: 10, with a score of: 0.586925060030743
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Each city may transform the core elements into series of concrete action items or stepping stones that take into account local conditions and follow logical sequence

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Together these stepping stones enable city to develop its own unique action plan called an Eco pathway Figure

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The Eco Cities Initiative also introduces cities to methods and tools that will lead to more effective decision making through powerful diagnostics and scenario planning

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These methods and tools can also be used to operationalize the core elements and implement the stepping stones

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FIG

Label: 0, with a score of: 1

Example of Phased Incremental Eco Pathway URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS In this context the ideal situation is when city adopts the four key principles applies the analytical and operational framework to its particular context and by doing so develops and begins to implement its own sustainability pathway

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Cities may begin incrementally by engaging in capacity building and data management and by initially targeting their most critical priority through developing and implementing an Eco catalyst project

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Unlike stand alone projects in resource efficiency catalyst project is distinguished by an explicit objective and an ability beyond the immediate project scope and objectives to drive the city forward on its sustainability pathway by catalyzing the process of change

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Moving Forward Together The World Bank is currently collaborating with cities in developing countries their national governments the international community global best practice cities multilateral and bilateral development agencies academia the private sector and NGOs

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As pilot Eco Cities in developing countries conceptualize and implement their own Eco pathways we hope to channel their support to other cities beginning their own Eco pathway

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At present national knowledge sharing and capacitydevelopment activities are being implemented under the Eco framework in Indonesia Vietnam and the Philippines

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ANNEX ANNEX Sustainable Development Goals To achieve the goals of Rio in an ambitious time bound and accountable manner we call upon governments in accordance with human rights the principle of common but differentiated responsibilities and respective capabilities to adopt the following draft Sustainable Development Goals together with the sub goals reasons and clarifications relating to each goal

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Sustainable Consumption and Production

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Sustainable Livelihoods Youth Education

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Climate Sustainability

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Clean Energy

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Biodiversity

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Water [Efficiency] The goals below are aspirational

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goal0NN

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While some of these are based on commitments already made by governments and other stakeholders others are proposed on the basis of advanced thinking among civil society organizations

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Healthy Seas and Oceans Blue Economy

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Healthy Forests

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Sustainable Agriculture

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Green Cities

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Subsidies and Investment

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New Indicators of Progress

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progress0.0591643007226199NN

Label: 3, with a score of: 0.491577064366258
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progress0.0449274832628543NN

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Access to Information

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Public Participation

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participation0.0614198493690006NN

Label: 11, with a score of: 0.32139432392271
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Access to Redress and Remedy

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Environmental Justice for the Poor and Marginalized

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Basic Health Source Reprinted from Rio UN Conference on Sustainable Development website http www

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uncsd

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org rio index

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page view nr type menu URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS ANNEX Earth Observation for Urban Monitoring What Urban Parameters Can Be Measured With Earth Observation

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The basic Earth observation EO datasets that are relevant to urban areas are land cover maps and their changes over time

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basic0.0583751976558017JJ
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These maps characterize the extent of urban areas together with the spatial and temporal distribution of specific urban land uses such as housing industry green areas and so on

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An example of an existing database with information on land use and land cover changes is the European Coordination of Information on the Environment CORINE program and more specifically the Urban Atlas CORINE component dedicated to urban mapping

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Urban Atlas maps are developed at geometric resolution of with minimum mapping unit MMU of

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hectares within the built up urban areas and lower resolution of hectare MMU outside urban centers

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Label: 15, with a score of: 0.588255290710695
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They are produced with urban and non urban classes which offers significantly better resolution than the CORINE classes Figure

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Making Use of Earth Observation Capabilities It is estimated that public institutional users generate more than percent of the market demand for earth observation data and services Keith and FIG

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Comparison of CORINE Land Cover Map with an Urban Atlas Map for the Same Area Bochinger

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land0.0389167984372011NN
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urban0.27622591898666JJ

Label: 15, with a score of: 0.441444103360459
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Free or low cost EO data solutions are preferred where available

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However significant step forward for the application of EO to urban development monitoring was the availability of commercial high and very high resolution VHR optical satellites with pixel size of

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meter

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This provides better information accuracy delivery time and continuity of datasets

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In addition the recent launch of very high resolution synthetic aperture radar SAR commercial missions in Europe and Canada is pushing forward range of applications in the urban risk management domain for example assessing risks of flooding land subsidence and landslides

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This development is of interest both to the public sector for example civil protection authorities and local municipalities as well as to the private sector for example insurance and engineering firms

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The European Urban Atlas is another example of an operational EO application that provides homogeneous and up to date information on more than European cities

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It offers comparable information on the density of residential areas commercial and industrial zones the extent of green areas exposure to flood risks and monitoring of urban sprawl essential for public transport and infrastructure planning in urban and peri urban areas

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This type ANNEX FIG

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type0.0688661930304451NN

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Monitoring Urban Infrastructure Subsidence in Ho Chi Minh City and Jakarta using SatelliteBased PINSR Interferometry SAR Source data from Altamira Information for European Space Agency and World Bank

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of information is routinely used by city governments and other European institutions such as DG REGIO DG ENV and the European Environment Agency

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At present the Urban Atlas maps are also being used to produce set of derived city indicators for example land cover and use green urban area per inhabitant urban sprawl index and so on

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In addition they are being used as input data for the modeling of urban vulnerability to natural hazards

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' What are the drivers of urban and other land development and what new infrastructure will be needed to support this development

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The combination of EO and other datasets can answer many and varied policy questions such as To support disaster risk management highresolution optical and radar imagery can be used in combination to map and identify physical changes in urban centers resulting from natural disasters

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This can be done for entire urban zones or at the level of individual buildings

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' How are cities changing over time

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' How much and in what proportion of uses is land being consumed for urban development

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WordScorePoS
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Label: 2, with a score of: 0.390368728337497
WordScorePoS
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Label: 15, with a score of: 0.310763377274578
WordScorePoS
proportion0NN
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' Where are the areas with the most significant land use change

Label: 13, with a score of: 0.83664428325409
WordScorePoS
land0NN
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Label: 15, with a score of: 0.350326570231689
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http dataservice

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eea

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europa

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eu map UrbanAtlasBeta http moland

Label: 0, with a score of: 1

jrc

Label: 0, with a score of: 1

ec

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europa

Label: 0, with a score of: 1

eu evdab HTML home

Label: 0, with a score of: 1

html ' What are the possible effects of natural disasters and how much of the population and assets will be affected

Label: 11, with a score of: 0.619093331873548
WordScorePoS
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Label: 10, with a score of: 0.417013570726034
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Label: 6, with a score of: 0.360943679213269
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Use of Earth Observation in Urban Disaster Risk Management unique capability of SAR is to measure changes in the land surface position to high accuracy using long time series of EO data

Label: 11, with a score of: 0.752698593286251
WordScorePoS
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Label: 15, with a score of: 0.343719637840065
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This method is being used to measure land subsidence Figure land disturbances following earthquakes and the dynamics of URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS FIG

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Label: 15, with a score of: 0.479406483100481
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Label: 2, with a score of: 0.374182937934324
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Label: 1, with a score of: 0.314855656847889
WordScorePoS
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Flood Risk Scenarios Developed for Rio de Janeiro Source software and data from European Space Agency and World Bank

Label: 2, with a score of: 0.516668134702521
WordScorePoS
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Label: 17, with a score of: 0.461580010004068
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active volcanoes

Label: 17, with a score of: 1
WordScorePoS
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With the new very high resolution commercial radar missions the motion and stability of individual buildings and structures can be measured to an accuracy of few millimeters per year

Label: 17, with a score of: 0.755878014574099
WordScorePoS
commercial0JJ
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This type of information is providing major new insights into identifying and understanding urban risk

Label: 11, with a score of: 0.803386629470173
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Finally the combined use of Earth observation satellite imagery optical and radar in situ data and advanced modeling techniques can support different phases of the urban risk management cycle with assessments of exposure of specific infrastructure to multi hazard risk as well as the level of potential loss

Label: 11, with a score of: 0.649301362298989
WordScorePoS
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Label: 9, with a score of: 0.318461434612967
WordScorePoS
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Label: 12, with a score of: 0.376848094504401
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For example the EO based flood simulation conducted for the watersheds of Rio Grande and Rio Anil in the Municipality of Rio de Janeiro Figure improved the understanding of the consequences of land cover changes vis à vis different land use scenarios the number of inhabitants affected by floods and the amount of time available for the civil protection authorities to respond in case of emergency

Label: 0, with a score of: 1

This type of information can be useful in formulating disaster prevention strategies

Label: 11, with a score of: 0.54942529662536
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Label: 3, with a score of: 0.40502237569098
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Label: 1, with a score of: 0.334461704582592
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Label: 5, with a score of: 0.310725306425191
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Future Directions in Earth Observation Capabilities Web based Earth observation together with GIS has opened new possibilities for developing specialized applications that are relevant to decision makers

Label: 14, with a score of: 0.691697395245215
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In the European space sector the Global Monitoring for Environment and Security GMES flagship program is being implemented in partnership between the European Union and the European Space Agency ESA

Label: 17, with a score of: 0.841334950009366
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Label: 12, with a score of: 0.306252215347872
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GMES comprises fleet of EO satellites system that was purpose built for the provision of operational information services in the Land Marine Humanitarian Aid Atmosphere and Security domains

Label: 14, with a score of: 0.684600116379174
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Label: 2, with a score of: 0.34628376086226
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The system developed through ESA will provide long term decadal data continuity which is key to achieving widespread use and acceptance of EO based information services

Label: 17, with a score of: 0.563533489518474
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Label: 7, with a score of: 0.348294755780617
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Label: 1, with a score of: 0.346121321195404
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Label: 2, with a score of: 0.315474490099747
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In terms of information on the urban environment multispectral high resolution imaging mission meter resolution for land monitoring will provide continuous SPOTand Landsat type data for vegetation soil and water cover inland waterways and coastal areas

Label: 6, with a score of: 0.52596557925304
WordScorePoS
term0NN
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Label: 12, with a score of: 0.402947549280593
WordScorePoS
term0NN
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Label: 11, with a score of: 0.392116347877943
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Label: 14, with a score of: 0.351770060472951
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Label: 17, with a score of: 0.294567986274916
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GMES has the capability to observe global land cover at meter resolution every five days representing an unprecedented source of information that can be tapped

Label: 1, with a score of: 0.62418480201422
WordScorePoS
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Label: 15, with a score of: 0.37154023286088
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Label: 9, with a score of: 0.336899457835916
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Furthermore the GMES Sentinel Data Policy is based on principles of full and open access setting the trend in future data policies around the world

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Label: 10, with a score of: 0.439213039065775
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It is evident that EO can provide wealth of information related to the monitoring of urban areas and the development of larger urban agglomerations

Label: 11, with a score of: 0.88443998899997
WordScorePoS
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Such information is being used operationally by number of institutions and the new European GMES initiative will soon bring enhanced capabilities

Label: 16, with a score of: 0.691170603044687
WordScorePoS
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Label: 9, with a score of: 0.409738864330261
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Label: 17, with a score of: 0.333467703244648
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A comprehensive assessment should be carried out to determine exactly what and how these new sources of information can contribute to key activities in the urban domain and the promotion of sustainable cities

Label: 11, with a score of: 0.906912129188607
WordScorePoS
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ANNEX ANNEX Publishing City Information Proposed South African Cities Accord The World Bank released the report Cities and Climate Change An Urgent Agenda at COP in Cancun December

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Label: 13, with a score of: 0.373479006042756
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This included press conference press release printed copies and placement of the report on the Bank external website

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The report included table compiling city based per capita GHG emissions for some cities of which were suggested to be sufficiently comparable having been peer reviewed within the academic community

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Label: 13, with a score of: 0.171407683527245
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Public pick up of the report was considered good but relatively modest

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Label: 12, with a score of: 0.503699353131588
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Label: 11, with a score of: 0.420973903523647
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Label: 3, with a score of: 0.378256131505688
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The same table was included in paper in the April edition of the journal Environment and Urbanization

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This time however the paper publication was accompanied by an IIED press release drawing attention to the ranking of cities the table facilitated

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The press release was picked up by more than media outlets and translated into more than languages

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One media outlet that picked up the IIED press release was local newspaper in Cape Town

Label: 12, with a score of: 0.793181917418465
WordScorePoS
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The front page of the local newspaper included prominent headline highlighting that Cape Town per capita GHG emissions were higher than London s

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Considerable efforts by City Hall staff ensued trying to explain the numbers and identifying an error in the reported values Cape Town GHG emissions were

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Label: 13, with a score of: 0.283976824965953
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tonne capita not the

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Label: 8, with a score of: 0.314670298914838
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cap reported in the article despite obtaining values from credible peer reviewed journal

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After extensive discussions between Cape Town and World Bank staff the values were corrected and important lessons on city data and publication emerged

Label: 11, with a score of: 0.918227621969302
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Label: 2, with a score of: 0.303650362579696
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Considerable city information is available

Label: 11, with a score of: 0.985155507322258
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Similar to countries most of which are smaller than these cities this information needs to be published annually

Label: 11, with a score of: 0.985155507322258
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The information is simply too important not to be readily available in the public domain

Label: 5, with a score of: 0.52312622521505
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Label: 9, with a score of: 0.46900266538132
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Label: 12, with a score of: 0.36696554297019
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Label: 17, with a score of: 0.342401941425541
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Label: 11, with a score of: 0.32753814826348
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One of the proposals within this report is for the partners to publish the beast available data for at least the largest urban areas every year

Label: 11, with a score of: 0.874716135875136
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As this report highlights this increased focus on cities is to be expected as policy makers city dwellers and city managers themselves further appreciate the enormous importance of cities

Label: 11, with a score of: 0.990340513223055
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Much of the recent focus on city information has been on GHG emissions

Label: 11, with a score of: 0.9244698623821
WordScorePoS
focus0.0373517915079663NN
city0.607697021770652NN
information0NN
emission0.0373517915079663NN

Label: 13, with a score of: 0.303440473564487
WordScorePoS
focus0.0279982034978914NN
city0NN
information0NN
emission0.19598742448524NN

With the February announcement of an agreed ICLEI protocol as supported by WRI UNEP UN Habitat and the World Bank now undergoing ISO standards development this information should become more common and regularly published

Label: 9, with a score of: 0.54013007052343
WordScorePoS
agree0VB
support0.0277502419802315NN
habitat0NN
world0.00257642711761688NN
bank0NN
undergo0.0602029956716497VB
standard0.0454743001434393NN
development0.0330049940546426NN
information0.0675706773907211NN
common0JJ

Label: 17, with a score of: 0.450789003457326
WordScorePoS
agree0.0422721924574123VB
support0.0451108483130998NN
habitat0NN
world0.00418824502414673NN
bank0.036961556179878NN
undergo0VB
standard0NN
development0.0469463607292177NN
information0.0219686053934784NN
common0JJ

Efforts to develop common GHG standard and ensure regular collection and publication of data are illustrative

Label: 17, with a score of: 0.59459019905633
WordScorePoS
develop0VB
common0JJ
standard0NN
ensure0.00335331148065841VB
regular0JJ
collection0.048933054487825NN
datum0.0739231123597559NN

Cities need simple process to collect and publish their data

Label: 11, with a score of: 0.955308543704456
WordScorePoS
city0.607697021770652NN
simple0JJ
process0NN
datum0NN

Sources of this information now include the Global City Indicator Facility as supplied by participating cities UNEP PwC UN Habitat IBNET World Bank

Label: 11, with a score of: 0.950795438847558
WordScorePoS
source0NN
information0NN
include0VB
global0.00501208088147356JJ
city0.607697021770652NN
facility0NN
supply0NN
habitat0NN
world0.00626002771693937NN
bank0NN

However the best source for all city indicators remains the city itself

Label: 11, with a score of: 0.992458023425985
WordScorePoS
source0NN
city0.607697021770652NN
remains0NNS

City information is not owned by anyone similar to how banks collect our household financial data doctors much of our health data and schools our academic performance our personal data is increasingly dispersed and amassed electronically

Label: 11, with a score of: 0.716696467654234
WordScorePoS
city0.607697021770652NN
information0NN
bank0NN
household0NN
datum0NN
health0.0229054731708393NN
school0NN
performance0NN
personal0JJ

Label: 4, with a score of: 0.257336750021357
WordScorePoS
city0NN
information0.0182963616752599NN
bank0NN
household0NN
datum0NN
health0NN
school0.208127520578185NN
performance0NN
personal0JJ

Label: 3, with a score of: 0.227911168727822
WordScorePoS
city0NN
information0.00914534810831654NN
bank0NN
household0NN
datum0NN
health0.191387734041787NN
school0NN
performance0NN
personal0JJ

Certainly there are enormous privacy and accuracy issues but cities need to have clear policy on how open they are willing to have their data

Label: 11, with a score of: 0.937834920984566
WordScorePoS
privacy0NN
issue0NN
city0.607697021770652NN
policy0.0136980135278128NN
datum0NN

The data exists anyway cities should therefore work to ensure that they control the accuracy and publication of city based data

Label: 11, with a score of: 0.968025742907375
WordScorePoS
datum0NN
exist0.04477822029736VB
city0.607697021770652NN
ensure0.00501208088147356VB
control0NN
base0NN

The Global City Indicator Facility as supported by agencies such as ICLEI UCLG CDP WRI URBAN DEVELOPMENT SERIES KNOWLEDGE PAPERS UNEP UN Habitat OECD World Bank is structured to have as much control on data collection and publication by cities as possible

Label: 11, with a score of: 0.931829150791984
WordScorePoS
global0.00501208088147356JJ
city0.607697021770652NN
facility0NN
support0.0224752180723703NN
agency0NN
urban0.27622591898666JJ
development0.0100241617629471NN
knowledge0NN
habitat0NN
oecd0NN
world0.00626002771693937NN
bank0NN
structure0NN
control0NN
datum0NN
collection0NN

Efforts are also underway to have the methodologies subject to ISO standards as with the recently announced GHG emissions protocol

Label: 13, with a score of: 0.835132935489273
WordScorePoS
standard0NN
emission0.19598742448524NN

Cities are regularly approached by politicians citizens news media and external agencies to provide data

Label: 11, with a score of: 0.932417065827472
WordScorePoS
city0.607697021770652NN
approach0NN
media0NNS
external0JJ
agency0NN
provide0.0269743071558856VB
datum0NN

Many of these requests are duplicative

Label: 0, with a score of: 1

Almost all cities already have extensive data collection and publication programs

Label: 11, with a score of: 0.975097229600439
WordScorePoS
city0.607697021770652NN
datum0NN
collection0NN

These programs however would be benefit from consolidation and global harmonization where practicable

Label: 0, with a score of: 1

For example when the GCIF was being established detailed assessment of city indicators was completed for Sao Paul Porto Alegre Belo Horizonte Bogota Cali Montreal Toronto and Vancouver

Label: 11, with a score of: 0.985457562769576
WordScorePoS
establish0VB
city0.607697021770652NN
complete0JJ

The eight cities were collecting some indicators annually only were in common

Label: 11, with a score of: 0.993544094251024
WordScorePoS
city0.607697021770652NN
annually0RB
common0.055245183797332JJ

Obvious efficiencies are possible

Label: 0, with a score of: 1

During COP in Durban discussions were held with South African cities and City Accord on City Data was proposed by the cities of South Africa

Label: 11, with a score of: 0.993974602027126
WordScorePoS
hold0NN
south0RB
african0IN
city0.607697021770652NN
datum0NN
africa0IN

Label: 5, with a score of: 0.074561792064629
WordScorePoS
hold0.18234260265061NN
south0RB
african0IN
city0NN
datum0NN
africa0IN

The Accord would take advantage of the efforts of the Global City Indicator Facility of which Cape Town Durban and Johannesburg are members

Label: 11, with a score of: 0.980575578848224
WordScorePoS
global0.00501208088147356JJ
city0.607697021770652NN
facility0NN

So too would the Accord maximize linkages to organizations such as national city associations CDP ICLEI UCLG Metropolis etc

Label: 11, with a score of: 0.985481754224269
WordScorePoS
organization0NN
national0.00501208088147356JJ
city0.607697021770652NN

South African cities are well represented in membership of organizations such as Johannesburg and ICLEI African head office in Cape Town City Hall

Label: 11, with a score of: 0.993793802184854
WordScorePoS
south0RB
african0IN
city0.607697021770652NN
represent0VB
organization0NN

GHG emissions are relatively well defined e

Label: 13, with a score of: 0.906096780895015
WordScorePoS
emission0.19598742448524NN

Johaneburg Durban CDP efforts and Cape Town early participation in Carbonn

Label: 0, with a score of: 1

South African cities are also well represented in academic literature and ongoing research activities e

Label: 11, with a score of: 0.948032166396125
WordScorePoS
south0RB
african0IN
city0.607697021770652NN
represent0VB
research0NN
activity0NN

IPCC participation biodiversity programming ecosystems services reviews

Label: 15, with a score of: 0.823604288005755
WordScorePoS
participation0NN
biodiversity0.221714625071441NN
ecosystem0.134075577362426NN
service0.0127200410376242NN
review0NN

Label: 5, with a score of: 0.308075151760105
WordScorePoS
participation0.0465610847355851NN
biodiversity0NN
ecosystem0NN
service0.022416649027415NN
review0.0688661930304451NN

Finally South Africa and its cities are uniquely placed to serve as bridge across high medium and low income countries and their cities

Label: 11, with a score of: 0.972051663661812
WordScorePoS
south0RB
africa0IN
city0.607697021770652NN
serve0VB
medium0NN
income0NN
country0NN

Label: 10, with a score of: 0.133144172176452
WordScorePoS
south0RB
africa0IN
city0NN
serve0VB
medium0NN
income0.166475342664294NN
country0NN

Reflecting the urgent need for comprehensive and globally accepted city led collection and publication of city indicators South African Cities Accord is proposed

Label: 11, with a score of: 0.994462985636065
WordScorePoS
urgent0JJ
globally0RB
city0.607697021770652NN
lead0NN
collection0NN
south0RB
african0IN

The Accord would be led by the cities of Cape Town Durban and Johannesburg and supported by the South Africa Local Government Association

Label: 11, with a score of: 0.956847669068299
WordScorePoS
lead0NN
city0.607697021770652NN
support0.0224752180723703NN
south0RB
africa0IN
local0.0194583992186006JJ
government0NN

A set of principles and common approaches are proposed ' Cities should be given first opportunity to provide city indicator data

Label: 11, with a score of: 0.970754613258142
WordScorePoS
set0NN
principle0NN
common0.055245183797332JJ
approach0NN
city0.607697021770652NN
opportunity0.0164178645080222NN
provide0.0269743071558856VB
datum0NN

' Cites as defined as the constitutionally mandated most local government should be given an opportunity to review data prior to publication

Label: 17, with a score of: 0.689241784559331
WordScorePoS
mandate0NN
local0.0130185595639838JJ
government0.048933054487825NN
opportunity0NN
review0.036961556179878NN
datum0.0739231123597559NN

' As much as possible data should be published to assist in policy development and observe regional and international trends not to compare

Label: 17, with a score of: 0.743518575132321
WordScorePoS
datum0.0739231123597559NN
assist0.048933054487825VB
policy0.0641521854453136NN
development0.0469463607292177NN
regional0.0422721924574123JJ
international0.00648516099948979JJ
compare0VB

Label: 1, with a score of: 0.312419709551147
WordScorePoS
datum0NN
assist0VB
policy0.0450489732120807NN
development0.0164833460638534NN
regional0.0519476926891779JJ
international0.00531301439641424JJ
compare0VB

Label: 9, with a score of: 0.311434039518569
WordScorePoS
datum0.0454743001434393NN
assist0VB
policy0.011275328195446NN
development0.0330049940546426NN
regional0.0260040237236563JJ
international0.00265959362690358JJ
compare0VB

' Ideally all city data should first or simultaneously published by the city on their website or similar means

Label: 11, with a score of: 0.990074956613501
WordScorePoS
city0.607697021770652NN
datum0NN
means0NNS

' In larger urban areas each local government should collect and publish data and the urban area aggregated

Label: 11, with a score of: 0.954015127168554
WordScorePoS
urban0.27622591898666JJ
local0.0194583992186006JJ
government0NN
datum0NN

Prior to all local governments participating in urban areas the main city or alternative organization should publish the aggregated best available data

Label: 11, with a score of: 0.966332108652496
WordScorePoS
local0.0194583992186006JJ
government0NN
urban0.27622591898666JJ
city0.607697021770652NN
alternative0NN
organization0NN
datum0NN

' Data should be published annually with no more than six month delay in data collection and six months of compilation and verification

Label: 17, with a score of: 0.90773565543244
WordScorePoS
datum0.0739231123597559NN
annually0RB
collection0.048933054487825NN

' All external organizations should defer to cities and their designated agencies such as ANNEX GCIF however third party peer review and ISO standardized definitions and approaches should be available for all urban areas in excess of million inhabitants

Label: 11, with a score of: 0.971897804670742
WordScorePoS
external0JJ
organization0NN
city0.607697021770652NN
agency0NN
party0NN
review0NN
approach0NN
urban0.27622591898666JJ

may be necessary however the data as outlined in Table is considered to be sufficiently simple for any city of million plus residents to regularly collect many examples exist

Label: 11, with a score of: 0.957615628059439
WordScorePoS
datum0NN
simple0JJ
city0.607697021770652NN
exist0.04477822029736VB

' Ideally the collection and publication of city data should be an ongoing part of city management the public disclosure should not place an undue burden on cities

Label: 11, with a score of: 0.988001281701495
WordScorePoS
collection0NN
city0.607697021770652NN
datum0NN
management0.0687164195125178NN
public0.0583751976558017NN
burden0NN

' Existing and new applications of remote sensing from urban areas should inform cities being monitored and ideally give cities the opportunity to review and comment on the information prior to publication with at least three month review period

Label: 11, with a score of: 0.965722308011941
WordScorePoS
exist0.04477822029736VB
sense0NN
urban0.27622591898666JJ
city0.607697021770652NN
monitor0NN
opportunity0.0164178645080222NN
review0NN
information0NN
period0NN

' For cities in low income countries ongoing assistance for data collection and publication Previous Knowledge Papers in This Series Lessons and Experiences from Mainstreaming HIV AIDS into Urban Water AFTU AFTU Projects Nina Schuler Alicia Casalis Sylvie Debomy Christianna Johnnides and Kate Kuper September No

Label: 11, with a score of: 0.684593210293825
WordScorePoS
city0.607697021770652NN
income0NN
country0NN
assistance0.0373517915079663NN
datum0NN
collection0NN
previous0JJ
knowledge0NN
experience0NN
hiv0NN
aid0NN
urban0.27622591898666JJ
water0.0458109463416786NN
project0NN

Label: 3, with a score of: 0.449132852874226
WordScorePoS
city0NN
income0NN
country0NN
assistance0NN
datum0NN
collection0NN
previous0JJ
knowledge0NN
experience0.0249430985430622NN
hiv0.448149338755887NN
aid0.123094312756122NN
urban0JJ
water0.0382775468083574NN
project0NN

Label: 6, with a score of: 0.423493709677792
WordScorePoS
city0NN
income0NN
country0NN
assistance0NN
datum0NN
collection0NN
previous0JJ
knowledge0NN
experience0NN
hiv0NN
aid0NN
urban0JJ
water0.556409482163189NN
project0.041835863322702NN

Label: 12, with a score of: 0.179328163929282
WordScorePoS
city0NN
income0NN
country0NN
assistance0NN
datum0NN
collection0NN
previous0JJ
knowledge0NN
experience0NN
hiv0NN
aid0NN
urban0JJ
water0.186896403054502NN
project0.0664302389559493NN

Label: 10, with a score of: 0.175830851997563
WordScorePoS
city0NN
income0.166475342664294NN
country0NN
assistance0.0330412182586772NN
datum0NN
collection0NN
previous0JJ
knowledge0NN
experience0NN
hiv0NN
aid0NN
urban0.0488696285210061JJ
water0NN
project0NN

Occupational and Environmental Health Issues of Solid Waste Management Special Emphasis on Middle and Lower Income Countries Sandra Cointreau July No

Label: 12, with a score of: 0.439642283753319
WordScorePoS
environmental0.0598270388037045JJ
health0.0339811641917276NN
issue0NN
waste0.138532104381853NN
management0.0339811641917276NN
special0JJ
middle0JJ
income0NN
country0NN

Label: 10, with a score of: 0.435517652059724
WordScorePoS
environmental0.0237821918091848JJ
health0.0405241466500776NN
issue0NN
waste0NN
management0NN
special0.0330412182586772JJ
middle0JJ
income0.166475342664294NN
country0NN

Label: 3, with a score of: 0.378180762978878
WordScorePoS
environmental0JJ
health0.191387734041787NN
issue0.0249430985430622NN
waste0NN
management0.0127591822694525NN
special0JJ
middle0JJ
income0NN
country0NN

Label: 11, with a score of: 0.424992219381765
WordScorePoS
environmental0.0537696560159885JJ
health0.0229054731708393NN
issue0NN
waste0.0373517915079663NN
management0.0687164195125178NN
special0.0747035830159326JJ
middle0JJ
income0NN
country0NN

Label: 9, with a score of: 0.380823423530654
WordScorePoS
environmental0.0221298700466866JJ
health0.0377086395700813NN
issue0NN
waste0NN
management0NN
special0JJ
middle0.0602029956716497JJ
income0.110649350233433NN
country0NN

Review of Urban Development Issues in Poverty Reduction Strategies Judy L

Label: 1, with a score of: 0.590656160117282
WordScorePoS
review0NN
urban0JJ
development0.0164833460638534NN
issue0NN
poverty0.269969049497485NN
reduction0NN
strategy0.0442083771593746NN

Label: 11, with a score of: 0.588676951340354
WordScorePoS
review0NN
urban0.27622591898666JJ
development0.0100241617629471NN
issue0NN
poverty0.0164178645080222NN
reduction0.0268848280079943NN
strategy0NN

Label: 10, with a score of: 0.307708750613728
WordScorePoS
review0NN
urban0.0488696285210061JJ
development0.0177346522508396NN
issue0NN
poverty0.05809266143877NN
reduction0.0475643836183697NN
strategy0NN

Baker and Iwona Reichardt June No

Label: 0, with a score of: 1

Urban Poverty in Ethiopia Multi Faceted and Spatial Perspective Elisa Muzzini January No

Label: 11, with a score of: 0.668740516930503
WordScorePoS
urban0.27622591898666JJ
poverty0.0164178645080222NN
multus0NN
perspective0NN

Label: 1, with a score of: 0.616924916566577
WordScorePoS
urban0JJ
poverty0.269969049497485NN
multus0NN
perspective0NN

Urban Poverty Global View Judy L

Label: 11, with a score of: 0.66944589475882
WordScorePoS
urban0.27622591898666JJ
poverty0.0164178645080222NN
global0.00501208088147356JJ
view0NN

Label: 1, with a score of: 0.60717658722033
WordScorePoS
urban0JJ
poverty0.269969049497485NN
global0JJ
view0NN

Baker January No

Label: 0, with a score of: 1

Preparing Surveys for Urban Upgrading Interventions Prototype Survey Instrument and User Guide Ana Goicoechea April No

Label: 11, with a score of: 0.827021413448425
WordScorePoS
survey0NN
urban0.27622591898666JJ
upgrade0.0315914058171786NN
intervention0NN
user0NN

Exploring Urban Growth Management Insights from Three Cities Mila Freire Douglas Webster and Christopher Rose June No

Label: 11, with a score of: 0.942510043043093
WordScorePoS
urban0.27622591898666JJ
growth0NN
management0.0687164195125178NN
city0.607697021770652NN
rise0.0268848280079943NN

Label: 8, with a score of: 0.20068434818171
WordScorePoS
urban0JJ
growth0.208565600657229NN
management0NN
city0NN
rise0NN

Private Sector Initiatives in Slum Upgrading Judy L

Label: 11, with a score of: 0.508316036915419
WordScorePoS
private0JJ
sector0NN
initiative0NN
slum0.146277152173395NN
upgrade0.0315914058171786NN

Label: 17, with a score of: 0.493865373410583
WordScorePoS
private0.0898760357768464JJ
sector0.0459744293658165NN
initiative0.036961556179878NN
slum0NN
upgrade0NN

Label: 9, with a score of: 0.415611307115149
WordScorePoS
private0.0368585655748969JJ
sector0.0565629593551219NN
initiative0NN
slum0NN
upgrade0.0520080474473126NN

Label: 7, with a score of: 0.415353043025341
WordScorePoS
private0JJ
sector0NN
initiative0.0924643738905717NN
slum0NN
upgrade0.0528748274225028NN

Label: 5, with a score of: 0.322717520833099
WordScorePoS
private0.0558185411034276JJ
sector0.0571058915334038NN
initiative0NN
slum0NN
upgrade0NN

Baker and Kim McClain May No

Label: 0, with a score of: 1

The Urban Rehabilitation of the Medinas The World Bank Experience in the Middle East and North Africa Anthony G

Label: 11, with a score of: 0.877026938329616
WordScorePoS
urban0.27622591898666JJ
world0.00626002771693937NN
bank0NN
experience0NN
middle0JJ
north0RB
africa0IN

Bigio and Guido Licciardi May No

Label: 0, with a score of: 1

Cities and Climate Change An Urgent Agenda Daniel Hoornweg December No

Label: 13, with a score of: 0.725821040152986
WordScorePoS
city0NN
climate0.291881734282273NN
change0.402565546215053NN
urgent0.0335649154577099JJ
agenda0NN
december0.0548233071065585NNP

Label: 11, with a score of: 0.613965851001784
WordScorePoS
city0.607697021770652NN
climate0.0229054731708393NN
change0.0315914058171786NN
urgent0JJ
agenda0NN
december0NNP

Memo to the Mayor Improving Access to Urban Land for All Residents Fulfilling the Promise Barbara Lipman with Robin Rajack June No

Label: 11, with a score of: 0.868423680479091
WordScorePoS
improve0.0273960270556257VB
access0.00626002771693937NN
urban0.27622591898666JJ
land0.0389167984372011NN

Conserving the Past as Foundation for the Future China World Bank Partnership on Cultural Heritage Conservation Katrinka Ebbe Guido Licciardi and Axel Baeumler September No

Label: 17, with a score of: 0.757646242701419
WordScorePoS
conserve0VB
foundation0NN
future0NN
world0.00418824502414673NN
bank0.036961556179878NN
partnership0.3914644359026NN
cultural0JJ
heritage0NN
conservation0NN

Label: 14, with a score of: 0.38463067710808
WordScorePoS
conserve0.0803666323453555VB
foundation0NN
future0.0543366405761884NN
world0.0045533140757086NN
bank0NN
partnership0NN
cultural0JJ
heritage0NN
conservation0.0803666323453555NN

Label: 11, with a score of: 0.301219407721485
WordScorePoS
conserve0VB
foundation0NN
future0.0373517915079663NN
world0.00626002771693937NN
bank0NN
partnership0NN
cultural0.055245183797332JJ
heritage0.0731385760866977NN
conservation0NN

Guidebook on Capital Investment Planning for Local Governments Olga Kaganova October No

Label: 17, with a score of: 0.713761956287066
WordScorePoS
investment0.0766240489430275NN
plan0.0130185595639838NN
local0.0130185595639838JJ
government0.048933054487825NN

Financing the Urban Expansion in Tanzania Zara Sarzin and Uri Raich January No

Label: 11, with a score of: 0.981279798055873
WordScorePoS
financing0NN
urban0.27622591898666JJ
expansion0.0731385760866977NN

What Waste Global Review of Solid Waste Management Daniel Hoornweg and Perinaz Bhada Tata March No

Label: 12, with a score of: 0.778415540375261
WordScorePoS
waste0.138532104381853NN
global0.022306853314744JJ
review0NN
management0.0339811641917276NN

Investment in Urban Heritage Economic Impacts of Cultural Heritage Projects in FYR Macedonia and Georgia David Throsby Macquarie University Sydney September No

Label: 11, with a score of: 0.797643373214431
WordScorePoS
investment0NN
urban0.27622591898666JJ
heritage0.0731385760866977NN
economic0.0389167984372011JJ
impact0.0194583992186006NN
cultural0.055245183797332JJ
project0NN
university0NN

Label: 8, with a score of: 0.228626002752341
WordScorePoS
investment0.0252035516550537NN
urban0JJ
heritage0NN
economic0.128463821595706JJ
impact0NN
cultural0JJ
project0NN
university0NN

SDGCOUNT
1380
2232
3106
4131
5162
6207
7537
8442
9522
10380
111940
12850
13632
14119
15150
16224
17832

RelationCOUNT
1,1090
1,11189
1,1266
1,1349
1,1410
1,1522
1,1634
1,1789
1,229
1,325
1,46
1,532
1,620
1,727
1,8105
1,951
10,11179
10,1282
10,1366
10,146
10,158
10,1624
10,1779
11,12333
11,13334
11,1439
11,1567
11,16110
11,17365
12,13158
12,1447
12,1533
12,1640
12,17210
13,1420
13,1526
13,1633
13,17109
14,1519
14,166
14,1727
15,167
15,1717
16,17122
2,1018
2,1168
2,1252
2,1327
2,1420
2,1521
2,168
2,1795
2,38
2,45
2,55
2,627
2,730
2,819
2,943
3,1017
3,1151
3,1239
3,1314
3,142
3,152
3,165
3,1716
3,412
3,512
3,616
3,715
3,812
3,915
4,1011
4,1150
4,1229
4,1312
4,146
4,152
4,1615
4,1730
4,59
4,68
4,711
4,820
4,929
5,1020
5,1154
5,1225
5,135
5,142
5,156
5,1620
5,1771
5,65
5,713
5,829
5,949
6,1017
6,1183
6,12152
6,1337
6,1411
6,1516
6,1612
6,1741
6,753
6,823
6,925
7,1023
7,11202
7,12357
7,13166
7,1419
7,1513
7,1615
7,17179
7,856
7,9112
8,10140
8,11243
8,12145
8,1373
8,1413
8,1518
8,1636
8,1794
8,996
9,1085
9,11200
9,12157
9,1359
9,1418
9,1516
9,1625
9,17177